Method and system for computer-aided aneurysm triage

ABSTRACT

A system for computer-aided triage includes and/or interfaces with a computing system. A method for computer-aided triage includes receiving a data packet including a set of images; and processing the set of images to determine a suspected condition and/or associated features. Additionally or alternatively, the method can include any or all of: preprocessing the set of images; triggering an action based on the suspected condition and/or associated features; determining a recipient based on the suspected condition; preparing a data packet for transfer; transmitting information to a device associated with the recipient; receiving an input from the recipient and triggering an action based on the input; aggregating data; and/or any other suitable processes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/385,326, filed 26 Jul. 2021, which claims the benefit of U.S.Provisional Application No. 63/056,347, filed 24 Jul. 2020, which isincorporated in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the medical triage field, and morespecifically to a new and useful system and method for computer-aidedaneurysm triage in the medical triage field.

BACKGROUND

In current triaging workflows, especially those in an emergency setting,a patient presents at a first point of care, where imaging is performed.The image data is then sent to a standard radiology workflow, whichtypically involves: images (equivalently referred to herein asinstances) being uploaded to a radiologist's queue, the radiologistreviewing the images at a workstation, the radiologist generating areport, an emergency department doctor reviewing the radiologist'sreport, the emergency department doctor determining a specialist tocontact, and making a decision of how to treat and/or transfer thepatient (e.g., to a 2^(nd) point of care). This workflow is typicallyvery time-consuming, which increases the time it takes to treat and/ortransfer a patient to a specialist.

This can be especially complicated in instances involving patientspresenting with an aneurysm, as not only are aneurysms often difficultto spot (e.g., due to their small size), but the next steps fortreatment are often ambiguous and subjective. In some cases, forinstance, an aneurysm might be left untreated with no explicit plans forfollow-up imaging made, which could potentially cause dangerous andsometimes fatal consequences. In other cases, the aneurysm might not bedetected at all in the conventional workflow (e.g., due to its smallsize).

Thus, there is a need in the medical triage field to create an improvedand useful system and method for decreasing the time it takes toidentify and initiate treatment for a patient presenting with a criticalcondition such as an aneurysm.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic of a system for computer-aided triage.

FIG. 2 is a schematic of a method for computer-aided triage.

FIG. 3 is a schematic of an algorithm for computer-aided aneurysmdetection.

FIG. 4 depicts a variation of a method for computer-aided triage.

FIGS. 5A-5B depict a variation of an application on a user device.

FIG. 6 depicts a variation of a method for computer-aided triage.

FIG. 7 depicts a variation of the method involving recommending thepatient for a clinical trial.

FIG. 8 depicts a variation of a notification transmitted to a device ofa participant.

FIG. 9 depicts a variation of a notification and subsequent workflow ofrecommending a patient for a clinical trial.

FIG. 10 depicts a variation of the system.

FIG. 11 depicts variations of a client application executing on a firstand second user device.

FIG. 12 depicts a variation of a method for the computer-aided detectionof one or more suspected aneurysms.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview

As shown in FIG. 1, a system 100 for computer-aided triage includesand/or interfaces with a computing system. Additionally oralternatively, the system 100 can include and/or interface with any orall of: a router, a client application, any number of computing systems(e.g., local, remote), servers (e.g., PACS server), storage, lookuptable, memory, and/or any other suitable components or combination ofcomponents. Further additionally or alternatively, the system caninclude any or all of the components, embodiments, and examples asdescribed in any or all of: U.S. application Ser. No. 16/012,458, filed19 Jun. 2018; U.S. application Ser. No. 16/012,495, filed 19 Jun. 2018;U.S. application Ser. No. 16/688,721, filed 19 Nov. 2019; U.S.application Ser. No. 16/913,754, filed 26 Jun. 2020; U.S. applicationSer. No. 16/938,598, filed 24 Jul. 2020; and U.S. application Ser. No.17/001,218, filed 24 Aug. 2020; each of which is incorporated herein inits entirety by this reference.

As shown in FIG. 2, the method 200 includes receiving a data packetincluding a set of images S205; and processing the set of images todetermine a suspected condition and/or associated features S220.Additionally or alternatively, the method 200 can include any or all of:preprocessing the set of images S210; triggering an action based on thesuspected condition and/or associated features S230; determining arecipient based on the suspected condition S232; preparing a data packetfor transfer S234; transmitting information to a device associated withthe recipient S236; receiving an input from the recipient and triggeringan action based on the input S238; aggregating data; and/or any othersuitable processes.

Further additionally or alternatively, the method 200 can include any orall of the processes, embodiments, and examples described in any or allof: U.S. application Ser. No. 16/012,458, filed 19 Jun. 2018; U.S.application Ser. No. 16/012,495, filed 19 Jun. 2018; U.S. applicationSer. No. 16/688,721, filed 19 Nov. 2019; U.S. application Ser. No.16/913,754, filed 26 Jun. 2020; U.S. application Ser. No. 16/938,598,filed 24 Jul. 2020; and U.S. application Ser. No. 17/001,218, filed 24Aug. 2020; each of which is incorporated herein in its entirety by thisreference, and/or any other suitable processes performed in any suitableorder. The method 200 can be performed with a system as described aboveand/or any other suitable system.

2. Benefits

The system and method for computer-aided aneurysm triage can conferseveral benefits over current systems and methods.

In a first set of variations, the system and/or method confer thebenefit of reducing the time to match and/or transfer a patientpresenting with a condition (e.g., aneurysm) to a specialist. In aspecific example, for instance, the average time between generating ascan and notifying a specialist (e.g., in a case associated with asuspected condition) is reduced from over 30 minutes (e.g., 35 minutes,40 minutes, 45 minutes, 50 minutes, greater than 50 minutes, etc.) toless than 8 minutes (e.g., 30 seconds, less than a minute, between 1-2minutes, 2 minutes, between 2-3 minutes, 3 minutes, between 3-4 minutes,etc.). This can additionally or alternatively function to reduce thetime it takes to match the patient with a clinical trial (e.g., tonotify a principal investigator associated with the clinical trial).

In these variations and others, numerous further benefits can beconferred, such as a reduced time to treatment, a reduced time totransferring to a 2^(nd) point of care (e.g., stroke center), a reducedtime to clinical trial approval and enrollment, an improved patientoutcome (e.g., upon detecting a small aneurysm, upon enabling themonitoring of a detected aneurysm, based on enabledcommunication/collaboration between members of the patient care team,etc.), and/or any other outcomes.

In a second set of variations, additional or alternative to the first,the system and/or method confer the benefit of enabling aneurysms,including small aneurysms (e.g., less than 5 mm, less than 4 mm, lessthan 3 mm, less than 2 mm, less than 1 mm, greater than 2 mm, etc.), tobe reliably detected. In specific examples, the method described belowenables aneurysms to be detected more reliably than by a radiologist(e.g., a junior radiologist, radiologist who might find a first aneurysmand stop looking for others, etc.). The system and/or method canadditionally or alternatively enable consistent treatment options to betriggered for patients found to have an aneurysm. In some examples, forinstance, a treatment option and/or associated specialist can betriggered based on the size and/or location of the detected aneurysm(e.g., aneurysms larger than 5 mm are treated, aneurysms larger than 3mm and on the basilar artery are treated, aneurysms less than apredetermined threshold are recommended for a follow up to monitor,etc.).

In a third set of variations, additional or alternative to thosedescribed above, the system and/or method provide a parallel process toa traditional workflow (e.g., standard radiology workflow), which canconfer the benefit of reducing the time to determine a treatment optionwhile having the outcome of the traditional workflow as a backup in thecase that an inconclusive or inaccurate determination (e.g., falsenegative, false positive, etc.) results from the method. Additionally oralternatively, the system and/or method can be implemented in place ofand/or integrated within a traditional workflow (e.g., the standardradiology workflow) and/or otherwise integrated with or independent fromany suitable workflows.

In a fourth set of variations, additional or alternative to thosedescribed above, the system and/or method confer the benefit ofminimizing the occurrence of false positive cases (e.g., less than 10%occurrence, less than 5% occurrence, which functions to minimizedisturbances caused to specialists or other individuals. This canfunction to minimize unnecessary disturbances to specialists invariations in which specialists or other users are notified on a mobiledevice upon detection of a potential aneurysm, as it can minimize theoccurrence of a specialist being alerted (e.g., potentially atinconvenient times of day, while the specialist is otherwise occupied,etc.) for false positives, while still maintaining a fallback in thestandard radiology workflow in the event that a true positive is missed.In a set of specific examples, the method includes training (e.g.,iteratively training) a set of deep learning models involved in aneurysmdetection on images originally detected to be a true positive but lateridentified as a false positive.

In a fifth set of variations, additional or alternative to thosedescribed above, the system and/or method confer the benefit ofreorganizing a queue of patients, wherein patients having a certaincondition are detected early and prioritized (e.g., moved to the frontof the queue).

In a sixth set of variations, additional or alternative to thosedescribed above, the system and/or method confer the benefit ofdetermining actionable analytics to optimize a workflow, such as atriage workflow.

In a seventh set of variations, additional or alternative to thosedescribed above, the system and/or method confer the benefit ofrecommending a patient for a clinical trial based on an automatedprocessing of a set of images (e.g., brain images) associated with thepatient.

In an eighth set of variations, additional or alternative to thosedescribed above, the system and/or method confer the benefit ofdetermining a suspected patient condition with a sensitivity of at least95% (e.g., 96%, 97%, between 96% and 97%, etc.) and a specificity of atleast 94% (e.g., 96%, 97%, between 96% and 97%, etc.).

In a ninth set of variations, additional or alternative to thosedescribed above, the system and/or method confer the benefit of enablingsmall aneurysms (e.g., diameter less than 5 mm, less than 4 mm, lessthan 3 mm, less than 2 mm, less than 1 mm, etc.) to be reliably detectedbased on CT images rather than MRI images.

Additionally or alternatively, the system and method can confer anyother benefits.

3. System

The system preferably interfaces with one or more points of care (e.g.,1^(st) point of care, 2^(nd) point of care, 3^(rd) point of care, etc.),each of which are typically a healthcare facility. A 1^(st) point ofcare herein refers to the healthcare facility at which a patientpresents, typically where the patient first presents (e.g., in anemergency setting). Conventionally, healthcare facilities include spokefacilities, which are often general (e.g., non-specialist, emergency,etc.) facilities, and hub (e.g., specialist) facilities, which can beequipped or better equipped (e.g., in comparison to spoke facilities)for certain procedures (e.g., mechanical thrombectomy), conditions(e.g., stroke), or patients (e.g., high risk). Patients typicallypresent to a spoke facility at a 1^(st) point of care, but canalternatively present to a hub facility, such as when it is evident whatcondition their symptoms reflect, when they have a prior history of aserious condition, when the condition has progressed to a high severity,when a hub facility is closest, randomly, or for any other reason. Ahealthcare facility can include any or all of: a hospital, clinic,ambulances, doctor's office, imaging center, laboratory, primary strokecenter (PSC), comprehensive stroke center (CSC), stroke ready center,interventional ready center, or any other suitable facility involved inpatient care and/or diagnostic testing.

A patient can be presenting with symptoms of a condition, no symptoms(e.g., presenting for routine testing), or for any other suitablesystem. In some variations, the patient is presenting with one or moresymptoms consistent with an aneurysm (e.g., visual disturbances such asloss of vision and/or double vision, pain above and/or around eye,numbness or weakness on side of face, difficulty speaking, headache,loss of balance, difficulty concentrating, problems with short-termmemory, etc.) and/or stroke (e.g., weakness, numbness, speechabnormalities, and facial drooping). Typically, these patients are thensent for an imaging protocol at an imaging modality, such as, but notlimited to: a non-contrast CT (NCCT) scan of the head, a CTA scan of thehead and neck, a CT perfusion (CTP) scan of the head.

A healthcare worker herein refers to any individual or entity associatedwith a healthcare facility, such as, but not limited to: a physician,emergency room physician (e.g., orders appropriate lab and imaging testsin accordance with a stroke protocol), radiologist (e.g., on-dutyradiologist, healthcare worker reviewing a completed imaging study,healthcare working authoring a final report, etc.), neuroradiologist,specialist (e.g., neurovascular specialist, vascular neurologist,neuro-interventional specialist, neuro-endovascular specialist,expert/specialist in a procedure such as mechanical thrombectomy,cardiac specialist, etc.), administrative assistant, healthcare facilityemployee (e.g., staff employee), emergency responder (e.g., emergencymedical technician), or any other suitable individual.

The image data can include computed tomography (CT) data (e.g.,radiographic CT, non-contrast CT, CT perfusion, etc.), preferablynon-contrast CT data (e.g., axial data, axial series of slices,consecutive slices, etc.) but can additionally or alternatively anyother suitable image data. The image data is preferably generated at animaging modality (e.g., scanner at the 1^(st) point of care), such as aCT scanner, magnetic resonance imaging (MRI) scanner, ultrasound system,or any other scanner. Additionally or alternatively, image data can begenerated from a camera, user device, accessed from a database orweb-based platform, drawn, sketched, or otherwise obtained.

3.1 System—Router 110

The system 100 can include a router no (e.g., medical routing system),which functions to receive a data packet (e.g., dataset) includinginstances (e.g., images, scans, etc.) taken at an imaging modality(e.g., scanner) via a computing system (e.g., scanner, workstation, PACSserver) associated with a 1^(st) point of care. The instances arepreferably in the Digital Imaging and Communications in Medicine (DICOM)file format, as well as generated and transferred between computingsystem in accordance with a DICOM protocol, but can additionally oralternatively be in any suitable format. Additionally or alternatively,the instances can include any suitable medical data (e.g., diagnosticdata, patient data, patient history, patient demographic information,etc.), such as, but not limited to, PACS data, Health-Level 7 (HL7)data, electronic health record (EHR) data, or any other suitable data,and to forward the data to a remote computing system.

The instances preferably include (e.g., are tagged with) and/orassociated with a set of metadata, but can additionally or alternativelyinclude multiple sets of metadata, no metadata, extracted (e.g.,removed) metadata (e.g., for regulatory purposes, HIPAA compliance,etc.), altered (e.g., encrypted, decrypted, deidentified, anonymizedetc.) metadata, or any other suitable metadata, tags, identifiers, orother suitable information.

The router no can refer to or include a virtual entity (e.g., virtualmachine, virtual server, etc.) and/or a physical entity (e.g., localserver). The router can be local (e.g., at a 1^(st) healthcare facility,2^(nd) healthcare facility, etc.) and associated with (e.g., connectedto) any or all of: on-site server associated with any or all of theimaging modality, the healthcare facility's PACS architecture (e.g.,server associated with physician workstations), or any other suitablelocal server or DICOM compatible device(s). Additionally oralternatively, the router can be remote (e.g., locate at a remotefacility, remote server, cloud computing system, etc.), and associatedwith any or all of: a remote server associated with the PACS system, amodality, or another DICOM compatible device such as a DICOM router.

The router 110 preferably operates on (e.g., is integrated into) asystem (e.g., computing system, workstation, server, PACS server,imaging modality, scanner, etc.) at a 1^(st) point of care butadditionally or alternatively, at a 2^(nd) point of care, remote server(e.g., physical, virtual, etc.) associated with one or both of the1^(st) point of care and the 2^(nd) point of care (e.g., PACS server,EHR server, HL7 server), a data storage system (e.g., patient records),or any other suitable system. In some variations, the system that therouter operates on is physical (e.g., physical workstation, imagingmodality, scanner, etc.) but can additionally or alternatively includevirtual components (e.g., virtual server, virtual database, cloudcomputing system, etc.).

The router 110 is preferably configured to receive data (e.g.,instances, images, study, series, etc.) from an imaging modality,preferably an imaging modality (e.g., CT scanner, MRI scanner,ultrasound machine, etc.) at a first point of care (e.g., spoke, hub,etc.) but can additionally or alternatively be at a second point of care(e.g., hub, spoke, etc.), multiple points of care, or any otherhealthcare facility. The router can be coupled in any suitable way(e.g., wired connection, wireless connection, etc.) to the imagingmodality (e.g., directly connected, indirectly connected via a PACSserver, etc.). Additionally or alternatively, the router can beconnected to the healthcare facility's PACS architecture, or otherserver or DICOM-compatible device of any point of care or healthcarefacility.

In some variations, the router includes a virtual machine operating on acomputing system (e.g., computer, workstation, user device, etc.),imaging modality (e.g., scanner), server (e.g., PACS server, server at1^(st) healthcare facility, server at 2^(nd) healthcare facility, etc.),or other system. In a specific example, the router is part of a virtualmachine server. In another specific example, the router is part of alocal server.

3.2 System—Remote Computing System 120

The system 100 can include a remote computing system 120, which canfunction to receive and process data packets (e.g., dataset fromrouter), determine a treatment option (e.g., select a 2^(nd) point ofcare, select a specialist, etc.), interface with a user device (e.g.,mobile device), compress a data packet, extract and/or remove metadatafrom a data packet (e.g., to comply with a regulatory agency), orperform any other suitable function.

Preferably, part of the method 200 is performed at the remote computingsystem (e.g., cloud-based), but additionally or alternatively all of themethod 200 can be performed at the remote computing system, the method200 can be performed at any other suitable computing system(s). In somevariations, the remote computing system 120 provides an interface fortechnical support (e.g., for a client application) and/or analytics. Insome variations, the remote computing system includes storage and isconfigured to store and/or access a lookup table, wherein the lookuptable functions to determine a treatment option (e.g., 2^(nd) point ofcare), a contact associated with the 2^(nd) point of care, and/or anyother suitable information.

In some variations, the remote computing system 120 connects multiplehealthcare facilities (e.g., through a client application, through amessaging platform, etc.).

In some variations, the remote computing system 120 functions to receiveone or more inputs and/or to monitor a set of client applications (e.g.,executing on user devices, executing on workstations, etc.).

3.3 System—Application 130

The system 100 can include one or more applications 130 (e.g., clients,client applications, client application executing on a device, etc.),such as the application shown in FIGS. 5A and 5B, which individually orcollectively function to provide one or more outputs (e.g., from theremote computing system) to a contact. Additionally or alternatively,the applications can individually or collectively function to receiveone or more inputs from a contact, provide one or more outputs to ahealthcare facility (e.g., first point of care, second point of care,etc.), establish communication between healthcare facilities, or performany other suitable function.

In some variations, one or more features of the application (e.g.,appearance, information content, information displayed, user interface,graphical user interface, etc.) are determined based on any or all of:the type of device that the application is operating on (e.g., userdevice vs. healthcare facility device, mobile device vs. stationarydevice), where the device is located (e.g., 1^(st) point of care, 2^(nd)point of care, etc.), who is interacting with the application (e.g.,user identifier, user security clearance, user permission, etc.), or anyother characteristic. In some variations, for instance, an applicationexecuting on a healthcare facility will display a 1^(st) set ofinformation (e.g., uncompressed images, metadata, etc.) while anapplication executing on a user device will display a 2^(nd) set ofinformation (e.g., compressed images, no metadata, etc.). In somevariations, the type of data to display is determined based on any orall of: an application identifier, mobile device identifier, workstationidentifier, or any other suitable identifier.

The outputs of the application can include any or all of: an alert ornotification (e.g., push notification, text message, call, email, etc.);an image set (e.g., compressed version of images taken at scanner,preview of images taken at scanner, images taken at scanner, etc.); aset of tools for interacting with the image set, such as any or all ofpanning, zooming, rotating, adjusting window level and width, scrolling,performing maximum intensity projection [MIP] (e.g., option to selectthe slab thickness of a MIP), changing the orientation of a 3D scan(e.g., changing between axial, coronal, and sagittal views, freestyleorientation change), showing multiple views of a set of images; aworklist (e.g., list of patients presenting for and/or requiring care,patients being taken care of by specialist, patients recommended tospecialist, procedures to be performed by specialist, etc.); a messagingplatform (e.g., HIPAA-compliant messaging platform, texting platform,video messaging, group messaging etc.); a telecommunication platform(e.g., video conferencing platform); a directory of contact information(e.g., 1^(st) point of care contact info, 2^(nd) point of care contactinfo, etc.); tracking of a workflow or activity (e.g., real-time or nearreal-time updates of patient status/workflow/etc.); analytics based onor related to the tracking (e.g., predictive analytics such as predictedtime remaining in radiology workflow or predicted time until strokereaches a certain severity; average time in a workflow; average time totransition to a second point of care, etc.); or any other suitableoutput.

The inputs can include any or all of the outputs described previously,touch inputs (e.g., received at a touch-sensitive surface), audioinputs, optical inputs, or any other suitable input. The set of inputspreferably includes an input indicating receipt of an output by arecipient (e.g., read receipt of a specialist upon opening anotification). This can include an active input from the contact (e.g.,contact makes selection at application), a passive input (e.g., readreceipt), or any other input.

In one variation, the system 100 includes a mobile device application130 and a workstation application 130—both connected to the remotecomputing system—wherein a shared user identifier (e.g., specialistaccount, user account, etc.) can be used to connect the applications(e.g., retrieve a case, image set, etc.) and determine the informationto be displayed at each application (e.g., variations of imagedatasets). In one example, the information to be displayed (e.g.,compressed images, high-resolution images, etc.) can be determined basedon: the system type (e.g., mobile device, workstation), the applicationtype (e.g., mobile device application, workstation application,), theuser account (e.g., permissions, etc.), any other suitable information,or otherwise determined.

The application can include any suitable algorithms or processes foranalysis, and part or all of the method 200 can be performed by aprocessor associated with the application.

The application preferably includes both front-end (e.g., applicationexecuting on a user device, application executing on a workstation,etc.) and back-end components (e.g., software, processing at a remotecomputing system, etc.), but can additionally or alternatively includejust front-end or back-end components, or any number of componentsimplemented at any suitable system(s).

3.4 System—Additional Components

The system 100 and/or or any component of the system 100 can optionallyinclude or be coupled to any suitable component for operation, such as,but not limited to: a processing module (e.g., processor,microprocessor, etc.), control module (e.g., controller,microcontroller), power module (e.g., power source, battery,rechargeable battery, mains power, inductive charger, etc.), sensorsystem (e.g., optical sensor, camera, microphone, motion sensor,location sensor, etc.), or any other suitable component.

4. Method

As shown in FIG. 2, the method 200 includes receiving a data packetincluding a set of images S205; and processing the set of images todetermine a suspected condition and/or associated features S220.Additionally or alternatively, the method 200 can include any or all of:preprocessing the set of images S210; triggering an action based on thesuspected condition and/or associated features S230; determining arecipient based on the suspected condition S232; preparing a data packetfor transfer S234; transmitting information to a device associated withthe recipient S236; receiving an input from the recipient and triggeringan action based on the input S238; aggregating data; and/or any othersuitable processes.

Further additionally or alternatively, the method 200 can include any orall of the processes, embodiments, and examples described in any or allof: U.S. application Ser. No. 16/012,458, filed 19 Jun. 2018; U.S.application Ser. No. 16/012,495, filed 19 Jun. 2018; U.S. applicationSer. No. 16/688,721, filed 19 Nov. 2019; U.S. application Ser. No.16/913,754, filed 26 Jun. 2020; U.S. application Ser. No. 16/938,598,filed 24 Jul. 2020; and U.S. application Ser. No. 17/001,218, filed 24Aug. 2020; each of which is incorporated herein in its entirety by thisreference, and/or any other suitable processes performed in any suitableorder. The method 200 can be performed with a system as described aboveand/or any other suitable system.

The method 200 is preferably performed separate from but in parallelwith (e.g., contemporaneously with, concurrently with, etc.) a standardradiology workflow (e.g., as shown in FIG. 4), but can additionally oralternatively be implemented within a standard workflow, be performed ata separate time with respect to a standard workflow, or be performed atany suitable time.

The method 200 can be partially or fully implemented with the system 100or with any other suitable system.

The method 200 functions to improve communication across healthcareworkers (e.g., specialists, members of a care team, etc.) and/orhealthcare facility networks (e.g., stroke networks, spokes and hubs,etc.) and increase the ability to detect (and optionally decrease thetime required to detect and/or transfer a patient) having a suspectedcondition (e.g., brain condition, aneurysm, un-ruptured aneurysm,stroke, hemorrhagic stroke, hemorrhage, intracerebral hemorrhage (ICH),ischemic stroke, large vessel occlusion (LVO), cardiac event, trauma,etc.). In some variations, the method functions to enable the transfer(and optionally decrease the time to transfer) a patient from a firstpoint of care (e.g., spoke, non-specialist facility, stroke center,ambulance, etc.) to a second point of care (e.g., hub, specialistfacility, comprehensive stroke center, etc.), wherein the second pointof care refers to a healthcare facility equipped to treat the patient.In some variations, the second point of care is the first point of care,wherein the patient is treated at the healthcare facility to which he orshe initially presents.

The method 200 can optionally function as a parallel workflow tool,wherein the parallel workflow is performed contemporaneously with (e.g.,concurrently, during, partially during) a standard radiology workflow(e.g., radiologist queue), but can additionally or alternatively beimplemented within a standard workflow (e.g., to automate part of astandard workflow process, decrease the time required to perform astandard workflow process, etc.), be performed during a workflow otherthan a radiology workflow (e.g., during a routine examination workflow),or at any other suitable time.

The method 200 is preferably performed in response to a patientpresenting at a first point of care. The first point of care can be anemergency setting (e.g., emergency room, ambulance, imaging center,etc.), equivalently referred to herein as an acute setting, or anysuitable healthcare facility, such as those described previously. Thepatient is typically presenting with (or suspected to be presentingwith), a neurovascular condition (e.g., aneurysm, un-ruptured aneurysm,stroke, etc.), cardiac event or condition (e.g., cardiovascularcondition, heart attack, etc.), trauma (e.g., acute trauma, blood loss,etc.), or any other condition (e.g., life-threatening condition,time-sensitive condition, non-time-sensitive condition, etc.). In othervariations, the method is performed for a patient presenting to aroutine healthcare setting (e.g., non-emergency setting, clinic, imagingcenter, etc.), such as for routine testing, screening, diagnostics,imaging, clinic review, laboratory testing (e.g., blood tests), or forany other reason.

Any or all of the method can be performed using any number of machinelearning (e.g., deep learning) or computer vision modules. Each modulecan utilize one or more of: supervised learning (e.g., using logisticregression, using back propagation neural networks, using randomforests, decision trees, etc.), unsupervised learning (e.g., using anApriori algorithm, using K-means clustering), semi-supervised learning,reinforcement learning (e.g., using a Q-learning algorithm, usingtemporal difference learning), and any other suitable learning style.Each module of the plurality can implement any one or more of: aregression algorithm (e.g., ordinary least squares, logistic regression,stepwise regression, multivariate adaptive regression splines, locallyestimated scatterplot smoothing, etc.), an instance-based method (e.g.,k-nearest neighbor, learning vector quantization, self-organizing map,etc.), a regularization method (e.g., ridge regression, least absoluteshrinkage and selection operator, elastic net, etc.), a decision treelearning method (e.g., classification and regression tree, iterativedichotomiser 3, C4.5, chi-squared automatic interaction detection,decision stump, random forest, multivariate adaptive regression splines,gradient boosting machines, etc.), a Bayesian method (e.g., naïve Bayes,averaged one-dependence estimators, Bayesian belief network, etc.), akernel method (e.g., a support vector machine, a radial basis function,a linear discriminate analysis, etc.), a clustering method (e.g.,k-means clustering, expectation maximization, etc.), an associated rulelearning algorithm (e.g., an Apriori algorithm, an Eclat algorithm,etc.), an artificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, bootstrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and any suitable form of machine learning algorithm. Each modulecan additionally or alternatively be a: probabilistic module, heuristicmodule, deterministic module, or be any other suitable module leveragingany other suitable computation method, machine learning method, orcombination thereof.

Each module can be validated, verified, reinforced, calibrated, orotherwise updated based on newly received, up-to-date measurements; pastmeasurements recorded during the operating session; historicmeasurements recorded during past operating sessions; or be updatedbased on any other suitable data. Each module can be run or updated:once; at a predetermined frequency; every time the method is performed;every time an unanticipated measurement value is received; or at anyother suitable frequency. The set of modules can be run or updatedconcurrently with one or more other modules, serially, at varyingfrequencies, or at any other suitable time. Each module can bevalidated, verified, reinforced, calibrated, or otherwise updated basedon newly received, up-to-date data; past data or be updated based on anyother suitable data. Each module can be run or updated: in response todetermination of an actual result differing from an expected result; orat any other suitable frequency.

Additionally or alternatively, the method 200 can function to recruitpatients for clinical trials (e.g., automatically based on processing aset of images and comparing with a set of clinical trial inclusioncriteria), establish communication between a clinical trial researchcoordinator and a specialist, approve a patient for a clinical trial(e.g., by consensus of multiple individuals in communication through theclient application), send a consent form to a patient, and/or performany other suitable functions.

4.1 Method—Receiving a Data Packet Including a Set of Images Associatedwith a First Point of Care S205

The method 200 includes receiving a data packet associated with apatient and taken at a first point of care S205, which functions tocollect data relevant to assessing a patient condition. Additionally oralternatively, S205 can function to initiate the method 200 (e.g., totrigger any or all of the subsequent processes of the method 200) and/orcan perform any other functions.

S205 is preferably performed initially in the method 200, but canadditionally or alternatively be performed in parallel with any otherprocesses of the method 200, in response to any other processes of themethod 200, multiple times, and/or at any other times. Additionally oralternatively, S205 can be performed at any other times and/or themethod 200 can be performed in absence of S205.

The data is preferably received at a router no, wherein the router is inthe form of a virtual machine operating on a computing system (e.g.,computer, workstation, quality assurance (QA) workstation, readingworkstation, PACS server, etc.) coupled to or part of an imagingmodality (e.g., CT scanner, MRI scanner, etc.), or any other suitablerouter. Additionally or alternatively, data can be received at a remotecomputing system (e.g., from an imaging modality, from a database, aserver such as a PACS server, an internet search, social media, etc.),or at any other suitable computing system (e.g., server) or storage site(e.g., database). In some variations, for instance, a subset of the data(e.g., image data) is received at the router while another subset of thedata (e.g., patient information, patient history, etc.) is received at aremote computing system. In a specific example, the data subset receivedat the router is eventually transmitted to the remote computing systemfor analysis.

S205 is preferably performed in response to (e.g., after, in real timewith, substantially in real time with, with a predetermined delay, witha delay of less than 10 seconds, with a delay of less than 1 minute, atthe prompting of a medical professional, etc.) the data (e.g., each of aset of instances) being generated at the imaging modality. Additionallyor alternatively, S205 can be performed in response to a set of multipleinstances being generated by the imaging modality (e.g., after a partialseries has been generated, after a full series has been generated, aftera study has been generated, etc.), in response to a metadata tag beinggenerated (e.g., for an instance, for a series, for a study, etc.), inresponse to a trigger (e.g., request for images), throughout the method(e.g., as a patient's medical records are accessed, as information isentered a server, as information is retrieved from a server, etc.), orat any other suitable time.

S205 can be performed a single time or multiple times (e.g.,sequentially, at different times in the method, once patient conditionhas progressed, etc.). In one variation, each instance is received(e.g., at a router, at a remote computing system, etc.) individually asit is generated. In a second variation, a set of multiple instances(e.g., multiple images, full series, etc.) are received together (e.g.,after a scan has completed, after a particular anatomical component hasbeen imaged, etc.).

The set of images are preferably in the form of computed tomographyangiograms (CTAs), from a CT scanner, but can additionally oralternatively include any other suitable images, such as—but not limitedto—any or all of: any suitable CT images (e.g., contrast CT images,non-contrast CT images, etc.); magnetic resonance imaging (MRI) images;and/or any other suitable images.

Image data is preferably received at the router (e.g., directly,indirectly, etc.) from the imaging modality (e.g., scanner) at which thedata was generated. Additionally or alternatively, image data or anyother data can be received from any computing system associated with thehealthcare facility's PACS server, any DICOM-compatible devices such asa DICOM router, or any other suitable computing system. The image datais preferably in the DICOM format but can additionally or alternativelyinclude any other data format.

In addition to or alternative to image data, the data can include blooddata, electronic medical record (EMR) data, unstructured EMR data,health level 7 (HL7) data, HL7 messages, clinical notes, or any othersuitable data related to a patient's medical state, condition, ormedical history.

The data preferably includes a set of one or more instances (e.g.,images), which can be unorganized, organized (e.g., into a series, intoa study, a sequential set of instances based on instance creation time,acquisition time, image position, instance number, unique identification(UID), other acquisition parameters or metadata tags, anatomical featureor location within body, etc.), complete, incomplete, randomly arranged,or otherwise arranged.

The data packet further preferably includes metadata associated with theset of images, such as, but not limited to, any or all of: one or morepatient identifiers (e.g., name, identification number, UID, etc.),patient demographic information (e.g., age, race, sex, etc.), reason forpresentation (e.g. presenting symptoms, medical severity score, etc.),patient history (e.g., prior scans, prior diagnosis, prior medicalencounters, etc.), medical record (e.g. history of present illness, pastmedical history, allergies, medications, family history, social history,etc.), scan information, scan time, scan type (e.g., anatomical regionbeing scanned, scanning modality, scanner identifier, etc.), number ofimages in scan, parameters related to scan acquisition (e.g.,timestamps, dosage, gurney position, scanning protocol, contrast bolusprotocol, etc.), image characteristics (e.g., slice thickness, instancenumber and positions, pixel spacing, total number of slices, etc.), orany other suitable information.

In some variations, S205 includes checking for a set of metadata and/orother image features, which functions to check for a set of inclusioncriteria. In a set of specific examples, this includes checking for anyor all of: a CTA scan of the head; an axial series of images; a slicethickness within a predetermined range (e.g., between 0.5 mm and 1 mm);an absence of missing slices; an alignment of instance numbers and/orpositions; an age of the patient (e.g., above a predeterminedthreshold); consistent pixel spacing; and/or any other suitableinclusion criteria. S205 can additionally or alternatively includefiltering images (e.g., images within a series, entire series, etc.)based on any or all associated metadata.

The method 200 can optionally further include transmitting any or all ofthe data packet to a computing system, such as a remote computingsystem, which functions to enable any or all of the subsequent processesto be performed at a remote computing system.

Each instance (e.g., image) of the dataset (e.g., image dataset) ispreferably sent individually as it is generated at an imaging modalityand/or received at a router, but additionally or alternatively, multipleinstances can be sent together after a predetermined set (e.g., series,study, etc.) has been generated, after a predetermined interval of timehas passed (e.g., instances sent every 10 seconds), upon the promptingof a medical professional, or at any other suitable time. Furtheradditionally or alternatively, the order in which instances are sent toa remote computing system can depend one or more properties of thoseinstances (e.g., metadata). Transmitting the data packet can beperformed a single time or multiple times (e.g., after each instance isgenerated), and can include transmitting any or all of: all of thedataset (e.g., image dataset and metadata), a portion of the dataset(e.g., only image dataset, subset of image dataset and metadata, etc.),or any other information or additional information (e.g., supplementaryinformation such as supplementary user information).

The data is preferably transmitted through a secure channel, furtherpreferably through a channel providing error correction (e.g., overTCP/IP stack of 1^(st) point of care), but can alternatively be sentthrough any suitable channel.

Prior to transmitting data to a computing system, any or all of thefollowing can be performed: encrypting any or all of the dataset (e.g.,patient information) prior to transmitting to the remote computingsystem, removing information (e.g., sensitive information),supplementing the dataset with additional information (e.g.,supplementary patient information, supplemental series of a study,etc.), compressing any or all of the dataset, or performing any othersuitable process.

In preferred variations, for patients presenting with and/or potentiallypresenting with an aneurysm, the data packet includes a set of CTA DICOMimages taken at a CT scanner, which are sent to a remote computingsystem for processing (e.g., as described below). Additionally oralternatively, images in any suitable format can be received from anysuitable scanner and/or imaging system (e.g., MRI scanner, ultrasoundsystem, X-Ray system, etc.).

4.2 Method—Preprocessing the Set of Images S210

The method 200 preferably includes preprocessing the set of images S210,which functions to prepare the images for subsequent process of themethod.

S210 is preferably performed in response to and based on S205, but canadditionally or alternatively be performed in response to any otherprocesses, prior to one or more processes, in parallel with one or moreprocesses, in absence of S215, and/or at any other times. Furtheradditionally or alternatively, the method 200 can be performed inabsence of S210.

S210 can optionally include organizing the set of images, such as basedon one or more type of metadata associated with the images. This canfunction to enable any or all of: easier, more accurate, and/or quickerprocessing; the selection of one or more models and/or algorithms forprocessing (e.g., based on patient metadata such as age and/or gender);the determination of a 2^(nd) point of care, procedure, and/orspecialist (e.g., based on metadata specifying 1^(st) point of care);resampling and/or otherwise preprocessing/processing the set of images;and/or can enable any other suitable actions. In variations where theset of images are sent to a remote computing system, the metadata can beread prior to transmitting to the remote computing system (e.g., at avirtual server coupled to the scanner and/or to a PACS system at thepoint of care), at the remote computing system, any combination, and/orat any other suitable times.

In a first set of variations, dimension metadata (e.g., spacing betweenpixels, spacing between slices, etc.) from the set of images are readand used to map and/or arrange the set of images in an array.

In a second set of variations additional or alternative to the firstset, patient demographic metadata are read and used to determine and/orselect a particular set of models, such as a set of models taking intoaccount and/or trained based on any or all of these metadata.

S210 can optionally include converting the image slices into an array ofintensities (e.g., Hounsfield unit [HU] values), which functions torepresent the set of images with intensity values for furtherprocessing. Additionally or alternatively, the set of images can beconverted into any other suitable arrays and/or types of information.

S210 can include checking for a set of exclusion criteria associatedwith the set of images and excluding the images from further processingin an event that any or all of the exclusion criteria are satisfied. Theexclusion criteria preferably include criteria associated with intensityvalues of the set of images, such as voxels having an HU value above apredetermined threshold (e.g., 3000 HU, between 2800 HU and 3000 HU,between 2500 HU and 3000 HU, between 3000 HU and 3200 HU, between 3000HU and 3500 HU, etc.), which can correspond to a metallic artifact(e.g., aneurysm clip). Additionally or alternatively, any or all of theexclusion criteria can be determined based on metadata (e.g., asdescribed above in the inclusion criteria), and/or any other suitableinformation. Checking for exclusion criteria can additionally oralternatively be performed in S205 and/or in any other process of themethod 200.

S210 preferably includes extracting regions and/or images of interestfrom the set of images, which functions to perform further processingonly on relevant information. This can include any or all of: extractingonly the images including the anatomy of interest (e.g., removing imagesbelow the head based on a fixed size threshold relative to the top ofthe scan); extracting soft matter (e.g., based on a set of HU valuethresholds, based on a skull stripping process, etc.); extractingvessels; and/or otherwise extracting regions and/or images of interest.

In preferred variations, this includes cropping any or all of the set ofimages, which function to reduce a region of the images that isprocessed in subsequent processes of the method. The images can becropped to any or all of: a predetermined size (e.g., based on apredetermined number of pixels, based on predetermined dimensions and/orarea, etc.); an inclusion of a predetermined number and/or percentage oflocations determined in a registration process (e.g., all locations,minimum area including all locations, at least 90% of all locations, aparticular subset of locations, etc.); a predetermined size andinclusion of all locations (e.g., a predetermined size centered on theset of locations, a predetermined border extending past the outermostlocations, etc.); and/or the images can be cropped in any other suitableways. The images can all be cropped to the same size, can be cropped todifferent sizes, can be filtered and eliminated (e.g., upon not havingany identified locations, upon having a number of locations below athreshold, etc.), and/or can be otherwise cropped or not cropped. Theimages can be cropped a single time throughout the method 200, multipletimes throughout the method 200, and/or at any suitable times throughoutthe method 200.

In some variations, for instance, each of the set of images is firstcropped (e.g., 220 mm cropped off the superior portion of the images) toremove portions not corresponding to brain.

S210 preferably includes clipping the set of images through a clippingtransformation process, which functions to remove HU values irrelevantto the suspected condition (e.g., irrelevant to an aneurysm) from theset of images. The irrelevant HU values to be clipped can correspond tohigh HU values, low HU values, or both. In preferred variations, forinstance, a first set of HU values below a predetermined threshold(e.g., between −500 and −520, less than −520, greater than −500, etc.)are clipped and a second set of HU values above a predeterminedthreshold (e.g., between 1000 and 1100, between 1000 and 1200, greaterthan 1200, less than 1000, etc.) are clipped.

S210 preferably includes normalizing the HU values of the set of images,which can function to enable optimal processing later in the method,enable comparisons to be made between multiple sets of images, and/orconfer any other suitable function. The HU values can be normalizedbased on any or all of: a mean HU value from the set of images (e.g.,after clipping), a median HU value, a standard deviation of the HUvalues, a predetermined HU value, and/or any other suitable value(s). Insome variations, the HU values are normalized by subtracting the mean HUvalue and dividing by the standard deviation. Additionally oralternatively, the HU values can be otherwise normalized and/or notnormalized.

In a first variation, S210 includes cropping the set of images toinclude the head region, clipping the set of images to remove relativelyhigh and relatively low HU values, and normalizing the HU values of theimages.

In a second variation, S210 includes cropping the set of images toinclude the head region, clipping the set of images to remove a skullfrom the images, and normalizing the HU values of the images.

Additionally or alternatively, S21 o be performed in absence of any orall of these pre-processing processes.

Further additionally or alternatively, S210 can include any othersuitable processes.

4.3 Method—Processing the Set of Images to Determine a SuspectedCondition and/or Associated Features S220

The method includes processing the set of images to determine asuspected condition and/or associated features S220, which functions toidentify a suspected condition in the set of images and to optionallydetermine one or more features associated with the suspected condition,any or all of which can be used in the determination and/or triggeringof an action related to care of the patient (e.g., as described below).

S220 is preferably performed in response to and based on S210, but canadditionally or alternatively be performed in response to any otherprocesses (e.g., S205), in parallel with one or more processes, prior toone or more processes, and/or at any other times. Further additionallyor alternatively, S220 can be performed in absence of S210, performedbased on information other than that received in S210 and/or S205,multiple times during the method 200, and/or the method 200 can beperformed in absence of S220.

The images are preferably processed based on a set of one or moretrained models (e.g., CNNs, deep 3D convolutional neural networks, feedforward deep CNNs, etc.) which are preferably trained (e.g., based onsupervised learning, based on unsupervised learning, based on acombination of supervised and unsupervised learning, etc.) to performany or all of: detecting the presence of a potential aneurysm,determining its location, determining an approximate segmentation of thepotential aneurysm, determining one or more scores (e.g., confidencescore, likelihood score, probability, etc.) associated with theaneurysm, and/or determine any other information. Additionally oralternatively, the model(s) can be any or all of: configured todetermine a portion of thee outputs; configured to determine otheroutputs (e.g., a severity of a potential aneurysm, other metricsassociated with the potential aneurysm, a specialist, a treatmentoption, etc.); be otherwise trained and/or structured (e.g., deeplearning models other than deep CNNs); and/or any other models can beimplemented to determine any suitable parameters.

S220 can optionally include resampling the images (e.g., pre-processedimages, cropped images produced in S205, output images of S205, originalimages, etc.) to a higher image resolution (e.g., to the inputresolution of the original scan, to a predetermined percentage of theinput resolution of the original scan, etc.), which preferably functionsto enable detection (e.g., fast detection, detection with reducedcomputing resources, etc.) of relatively small aneurysms (e.g., 1 mm indiameter or below, 1 mm or greater in diameter, between 1 mm and 3 mm indiameter, less than 5 mm in diameter, less than 4 mm in diameter, lessthan 3 mm in diameter, etc.). Additionally or alternatively, this canfunction to enable the performance of a registration process (e.g., asdescribed below) and/or can perform any other function(s). In specificexamples, for instance, this functions to provide a higher resolution toimages which have been cropped to a smaller region. Additionally oralternatively, uncropped and/or any other images can be resampled.Further additionally or alternatively, S220 can be performed in absenceof resampling the images.

The resolution to which the images are resampled to can be any or allof: predetermined, determined based on the resolution of the originalscan, dynamically determined, and/or otherwise determined. In a set ofpreferred variations, the set of images, which have been cropped toinclude the head, clipped, and normalized, are resampled to apredetermined resolution (e.g., 1 mm, 0.5 mm, between 0.5 and 1.5 mm, 2mm, less than 1 mm, greater than 1 mm, etc.) in all dimensions (e.g.,each voxel dimension, each pixel dimension, etc.). The predeterminedresolution is preferably associated with a minimum size of aneurysm(e.g., desired smallest size) for the method to detect, but canadditionally or alternatively be associated with a maximum size ofaneurysm for the method to detect, another size of aneurysm (e.g.,average size, median size, etc.), and/or any other size. Additionally oralternatively, the images can be resampled according to any otherparameters and/or desired dimensions. Further additionally oralternatively, the images can be processed without resampling, theimages can be downsampled, and/or the images can be otherwise processed.

S220 further preferably includes a cropping process (reference croppingprocess) prior to a registration process described below (e.g.,additional to a cropping in S210, in place of a cropping in S210, etc.),which functions to crop the resampled images in preparation forcomparison/registration with an atlas image. The cropping processpreferably includes a cropping in the z-axis (e.g., from the top of thehead in the scan) and a cropping in the x-y plane (e.g., from the centerof the scan), but can additionally or alternatively include any othersuitable cropping and/or no cropping. In specific examples, the imagesare cropped to narrow in on an region associated with the Circle ofWillis. Additionally or alternatively, the images can be otherwisesuitably cropped, and/or S220 can be performed in absence of this and/orany other cropping processes.

S220 includes performing a registration process, which functions toidentify and/or construct an anatomic (e.g., vasculature) region and/ora pathological region (e.g., region containing an aneurysm) of interest.This can additionally function to enable subsequent processes (e.g.,segmentation processes) of the method and/or reduce the processingrequirements of subsequent steps of the method (e.g., cropping to aregion of interest).

For variations involving brain conditions (e.g., aneurysms), theregistration process (e.g., through a set of neural networks) preferablyidentifies and outputs a predetermined set of brain locations (e.g.,based on an atlas). The predetermined set of brain locations preferablyincludes arbitrary locations, but can additionally or alternativelyinclude non-arbitrary (e.g., anatomically defined and/or categorized)locations, and/or any combination of arbitrary and non-arbitrary. Theset of brain locations preferably includes a plurality of locations(e.g., greater than 500, between 500 and 520, between 500 and 550,between 500 and 600, greater than 600, between 200 and 500, less than200, between 5 and 10, etc.), but can additionally or alternativelyinclude any number of locations, multiple subsets of location, and/orany other suitable locations.

The registration process can additionally or alternatively include anyor all of: rotating one or more images, translating one or images,scaling one or more images, and/or otherwise adjusting any or all of theimages.

The registration process is preferably performed with a set of trainedmodels (e.g., machine learning models, deep learning models, etc.), suchas through a set of one or more neural networks. The set of neuralnetworks preferably includes a set of one or more convolutional neuralnetworks (CNNs) (e.g., deep CNNs, feed forward deep CNNs, etc.), furtherpreferably CNNs with a U-Net and/or V-Net architecture. Additionally oralternatively, the set of neural networks can include CNNs with otherarchitecture(s), non-convolutional neural networks, recurrent neuralnetworks, recursive neural networks, and/or any other neural networks.Further additionally or alternatively, the registration process can beperformed with any number of rule-based, programmed, and/or manualprocesses.

In a set of preferred variations (e.g., as described below), theregistration process is performed with a neural network on a set ofbrain scans, which outputs a plurality of arbitrary brain locations(e.g., points) proximal to (e.g., located around, located within,partially overlapping with, fully overlapping with, encircling, etc.)the Circle of Willis, wherein the output effectively approximates theCircle of Willis. The plurality of locations can optionally beassociated with and/or configured for any number of constraints. In someexamples, for instance, to ensure that the key points collectively covera large volume in the brain (e.g., above a predetermined threshold, foraccuracy of the resulting points, etc.), a separation constraint can beimplemented in the neural networks (e.g., with a loss function) whichpenalizes pairs of predicted key points which are too close to eachother (e.g., below a predetermined distance threshold, below apredetermined distance threshold between 0-5 mm, below a predetermineddistance threshold of between 1-2 mm, below a predetermined distancethreshold of between 0.1 mm and 1 mm, etc.). Additionally oralternatively, any other constraints can be implemented and/or the keypoints can be identified in absence of constraints.

In specific examples, for instance, a neural network (e.g., deep CNNwith U-Net architecture) identifies a set of points (equivalentlyreferred to herein as key points) in one or more images (e.g., allimages in the series, all images received in S220, a subset of images, asingle most relevant image, etc.), wherein the key points preferablycorrespond to arbitrary points proximal to the Circle of Willis, but canadditionally or alternatively include anatomically-meaningful keypoints. The key points preferably correspond to a predetermined, fixednumber set of points (e.g., predetermined set of points in the referenceimages), but can additionally or alternatively include dynamicallydetermined points and/or any combination. The number of key points ispreferably configured to achieve a compromise between registrationquality and computational load, but can additionally or alternatively beotherwise determined. The number of key points is further preferablyconfigured to cover a relatively large volume of the brain, which can beenabled using a separation constraint, which functions to incorporate aterm in a loss function which penalizes pairs of predicted key pointsthat are relatively close to each other, but can additionally oralternatively be otherwise determined and/or configured.

Additionally or alternatively, any suitable key points can be selectedin any suitable way.

The registration process further preferably includes comparing the setof images (e.g., cropped region) with a similar region in a referenceatlas based on the set of locations (e.g., points) determined with theset of neural networks, which functions to determine (e.g., compute) atransformation to be applied to the set of images. In preferredvariations, an affine transformation is computed, but additionally oralternatively, any other suitable transformation can be computed.

The transformation is preferably an affine transformation, which can besolved for using a least-squares regression applied between the keypoints of the images and the same number of key points of the atlas,which effectively registers the scan to the atlas. Additionally oralternatively, any or other suitable transformation can be computed inany suitable way.

The registration process preferably additionally includes applying thecomputed transformation (e.g., affine transformation) to the set ofimages, which functions to enable subsequent processes of the method tobe accurately performed (e.g., anatomically correct aneurysm detected,location of aneurysm properly identified, etc.), such as a desiredanatomical region to be accurately cropped. The set of images to whichthe transformation is applied is preferably the entire input scanreceived in S205 (e.g., without any cropped regions, without anypreprocessing or processing, etc.) which functions perform the analysison the original images. Additionally or alternatively, thetransformation can be applied to any other set of images (e.g., imagesproduced in S210, images produced in S220, etc.).

In response to the transformation, S220 can optionally include croppingthe transformed images, which functions to narrow in on a region ofinterest for subsequent processes of the method 200. In preferredvariations, a region around the Circle of Willis (e.g., region includingthe Circle of Willis in its entirety, a region including at least aportion of the Circle of Willis, etc.) is cropped from the transformedimages. The cropping coordinates are preferably fixed and based on thelocation of the region of interest in the atlas, as the transformationhas aligned these coordinates with those of the atlas. Additionally oralternatively, the cropping region can be dynamically determined (e.g.,based on an identification of the Circle of Willis in the set of images,based on non-transformed images, etc.) and/or otherwise determined.

In a set of specific examples, a region containing the Circle of Willishaving predetermined dimensions of (e.g., 114 mm×112 mm×128 mm) iscropped from the transformed input set of images. Additionally oralternatively, a smaller region can result from the cropping, a largerregion can result from the cropping, and/or the cropping can beotherwise performed.

S220 can optionally include resampling the images after transformingand/or cropping the images (e.g., additional to the resampling performedabove, in place of the resampling performed above, etc.), which canfunction to enable even smaller aneurysms to be accurately detected(e.g., based on the smaller cropped region). In a set of variations, theimages are resampled to a resolution (e.g., voxel dimension) of 0.5 mm.Additionally or alternatively, the images can be resampled to aresolution greater than 0.5 mm, less than 0.5 mm, the images can bedownsampled, and/or S220 can be performed without this resamplingprocess.

S220 preferably includes segmenting the set of images, which functionsto identify and isolate one or more aneurysms. The segmentation ispreferably performed with a set of one or more trained models (e.g.,machine learning models, deep learning models, neural networks, etc.)configured to detect regions of the images associated with an aneurysm(e.g., hyperattenuated regions, regions having a particularshape/morphology, regions in a particular anatomical location, etc.).The set of trained models can include any or all of those describedabove, be different than any or all of those described above, be thesame (e.g., part of) any or all of those described above, and/or be ofany other type(s). In specific examples, for instance, the segmentationis performed with a feed-forward deep CNN (e.g., with a U-Netarchitecture). Additionally or alternatively, any or all of thesegmentation process can be performed with any other suitable models(e.g., trained models, rule-based models, programmed models, etc.)having any suitable architecture (e.g., V-nets, U-nets, etc.). Inpreferred variations, a feed-forward deep CNN with a U-Net architecturetrained to segment hyper-attenuated regions consisted with aneurysms isimplemented.

The models are preferably trained based on manual segmentations of scanspositive for aneurysms and scans negative for aneurysms, furtherpreferably with a cross-entropy loss function (e.g., computed over allvoxels). Additionally or alternatively, the deep learning models can betrained based on any or all of: augmented data (e.g., using any or allof: random rotations, scaling, translation, elastic deformation, andadditions of Gaussian noise; etc.), but can additionally oralternatively be trained with any suitable data and/or tools.

The deep learning models preferably receive a transformed, croppedregion (e.g., as described above) of the set of images as an input, butcan additionally or alternatively receive any other information (e.g., apreprocessed set of images, a processed set of images, etc.).

The segmentation preferably produces as an output a 3D array containingprobability values between 0 and 1 for each voxel, the probabilitycorresponding to the likelihood that each voxel within the scanrepresents a portion of an aneurysm, which functions to determine asuspected aneurysm. The probability values are further preferably thensummed, wherein the summed value is compared with a threshold and usedto make a determination of whether or not the scan contains an aneurysm.The threshold is preferably selected to establish both a relatively highsensitivity and a relatively high specificity (e.g., both above 91%,both above 90%, etc.), but can additionally or alternatively beotherwise selected. In preferred variations, the threshold is between 15and 25 (e.g., 20, 18, 22, etc.), but can additionally or alternativelybe 15 or less (e.g., 10, 12, etc.), 25 or greater (e.g., 26, 28, 30,etc.), or have any other suitable value. Further additionally oralternatively, an upper limit threshold can be used, the segmentationcan be otherwise performed, and/or any other thresholds or algorithmscan be used.

In some variations (e.g., as shown in FIG. 12), the segmentation processincludes a dynamic location segmentation process (equivalently referredto herein as a multi-stage segmentation process), wherein the dynamiclocation segmentation process involves predicting aneurysms within theset of images and refining, in multiple stages, these predictions.

The dynamic location segmentation process preferably includes predictingthe locations of one or more aneurysms in the set of images (e.g.,entire region resulting from pre-processing and/or registration) with aset of bounding boxes (e.g., 3D bounding boxes including sets of voxels,2D bounding boxes including sets of pixels, etc.), wherein each boundingbox includes a predicted segmentation of an aneurysm.

The dynamic location segmentation process is preferably an instancesegmentation method performed with a set of multiple neural networks,which can be any or all of: the same architecture (e.g., with differentweights, the same weights, etc.), different architectures, and/or anycombination, but can additionally or alternatively be in the form of anyother segmentation type. A first neural network (e.g., feed-forward deepCNN, other CNN, non-CNN, etc.) is preferably applied to the imagesand/or a region of the set of images (e.g., wherein each of a set ofregions is processed independently and the results merged), whichproduces a feature map defining a set of proposals for potentialdetected aneurysms, wherein each proposal preferably includes a scoreand bounding box. Each of these proposals is then progressively refinedthrough a second set of neural networks (e.g., same architecture,different architecture, etc.) performed in a set of multiple stages(e.g., with different weights at each stage). At each stage, the featuremap produced by the prior stage is processed to predict a set ofbounding boxes, thereby refining the bounding boxes predicted at theprior stage. Each stage of refinement preferably results in scores whichare higher in accuracy than the prior stage's scores, and fewerproposals are needed to keep false negatives low. Decreasing the numberof proposals allows for using a wider and/or deeper neural network toprocess them, as due to limited to computational resources, there is atradeoff between the number of proposals and the size (e.g., width anddepth) of the neural network. Additionally or alternatively, the neuralnetworks can be otherwise configured relative to the stage ofimplementation (e.g., decreasing size, same size, etc.). The last stagepreferably produces as an output a segmentation mask for the set ofimages, wherein the segmentation mask indicates a set of scoresassociated with predicted aneurysms remaining after the final stage.Determining this segmentation mask can optionally include checking thescores of the segmentation mask with a set of thresholds (e.g., where apredicted aneurysm associated with a summed score below a threshold canbe removed from consideration). Additionally or alternatively, any otheroutput(s) can be produced.

The second set of neural networks preferably includes at least 2 stages(e.g., 2, 3, 4, between 2-10, greater than 10, etc.), but canadditionally or alternatively include any number of stages.

In specific examples of the dynamic location segmentation process, afeed-forward deep CNN with a U-Net architecture is used for each of thefirst neural network and the second set of neural networks. Additionallyor alternatively, any other neural networks (e.g., other CNNs, otherarchitectures, etc.) or combination can be used.

Specific Examples: Feed-Forward Deep CNN with U-Net Architecture

In additional or alternative variations (e.g., as shown in FIG. 12), thesegmentation process includes a fixed location segmentation process(equivalently referred to herein as a single stage segmentationprocess), wherein the fixed location segmentation process involveschecking for aneurysms in a predetermined set of most likely locations.The locations are preferably located relative to the Circle of Willis,but can additionally or alternatively be otherwise defined and/orlocated. In specific examples, for instance, a set of predeterminedregions (e.g., between 1-10 regions, between 5-8 regions, 5, regions, 6regions, 7 regions, between 5-10 regions, between 10-20 regions, greaterthan 20 regions, etc.) is cropped from the set of images and each of thepredetermined regions processed with a neural network, wherein the setof predetermined regions has been found to contain more than apredetermined percentage (e.g., 90%, 95%, 99%, between 90-100%, etc.) ofaneurysms (e.g., in training data, in literature, in an aggregateddataset, etc.). Additionally or alternatively, the set of predeterminedregions can be otherwise selected.

Each of the predetermined regions is preferably processed with a CNN,further preferably a feed-forward deep CNN with a U-Net architecture(e.g., as described above). Additionally or alternatively, any otherneural networks can be used, any or all of the regions can be determinedwith different neural networks, and/or the regions can be otherwiseprocessed. In specific examples, each of the predetermined regions isprocessed with a feed-forward deep CNN, wherein each of these CNNsshares network weights but has a region-specific bias term learned foreach region. Additionally or alternatively, the neural networks can bedifferent, have different weights, not have any region-specific terms,and/or be otherwise defined.

The outputs of the fixed location segmentation process can optionally becombined (e.g., aggregated, summed, compared to select the highestscores, compared with thresholds, etc.), such as in an ensemble process(e.g., pixel-wise ensemble process, voxel-wise ensemble process, etc.).

Additionally or alternatively, in variations in which both the dynamicand fixed location segmentation processes are performed (e.g., inseries, in parallel, etc.), the scores from these processes canoptionally be aggregated (e.g., summed based on location, compared andused to determine a maximum score for each location, aggregated based onan equation, etc.). Additionally or alternatively, they can beindividually maintained (e.g., and individually compared with a set ofthresholds) and/or any combination.

In variations including multiple suspected aneurysms, the scores foreach aneurysm can be any or all of: independently determined, combined(e.g., summed together), used to calculate a combined score, and/orotherwise calculated and/or used. Additionally or alternatively, invariations including multiple processes (e.g., multiple independentprocesses) for detecting aneurysms (e.g., dynamic location segmentationprocess and fixed location segmentation process), the scores for thedetected aneurysms can be aggregated and/or otherwise processed (e.g.,summed, combined according to an equation, compared with a set ofmachine learning models, compared to determine highest scores, comparedwith a set of thresholds) to determine a final set of scores.

Any or all of the scores (e.g., as described above) are preferablycompared with a set of one or more thresholds (e.g., as describedabove), wherein in an event that scores exceed the threshold(s), asuspected aneurysm is detected/confirmed (e.g., and used to trigger oneor more actions). The scores compared with the threshold can be any orall of: associated with a single suspected aneurysm, associated with amultiple suspected aneurysms (e.g., aggregated scores), associated witha particular region of the set of images, associated with a particularsegmentation process (e.g., dynamic location segmentation process, fixedlocation segmentation process, etc.), associated with multiplesegmentation processes (e.g., aggregated scores from fixed location anddynamic location segmentation processes), and/or any other scores.Additional or alternative to seeing if the scores exceed a threshold,the method can include seeing if the scores are below a threshold (e.g.,for further investigation), comparing scores with a decision tree and/orlookup table, processing the scores with a model and/or algorithm and/orequation, and/or otherwise processing and/or interpreting the scores.

Any or all of the set of trained models can optionally additionally oralternatively be used to determine features and/or parameters associatedwith a suspected aneurysm or other suspected condition, such as, but notlimited to, any or all of: size features (e.g., aneurysm diameter,aneurysm cross-sectional area, aneurysm volume, number of voxels makingup aneurysm, number of pixels making up aneurysm, etc.); temporalfeatures (e.g., predicted time until rupture, predicted time sincerupture, etc.); one or more supplementary scores (e.g., risk scoreassociated with aneurysm, severity score associated with aneurysm,etc.); and/or any other features. Trained models (e.g., as describedabove, as described below, etc.) can additionally or alternatively beused to determine one or more actions (e.g., as described below), suchas any or all of: a treatment option (e.g., procedure type, aneurysmcoil size, etc.); a recipient to notify (e.g., particular specialist)based on a suspected aneurysm; a recommended path with which to accessthe aneurysm; and/or can perform any other suitable actions.

Further additionally or alternatively, any or all of the actions,associated features, and/or the detection of the suspected condition canbe performed in absence of trained models, with a combination of trainedmodels and un-trained tools (e.g., rule-based models, programmed models,decision trees, manual input and/or manual processes, lookup tables,algorithms, equations, etc.), and/or any other tools.

In a first variation, S220 includes resampling the images; cropping theimages; registering the images through the computation and applicationof a transformation, wherein the transformation is based on a set of keypoints determined based on a deep learning model; cropping the imagesbased on the transformation; resampling the cropped image to a higherresolution; segmenting the cropped images to identify one or moreaneurysms; calculating a score based on the segmentation; and comparingthe score with a predetermined threshold, wherein if the score is abovethe predetermined threshold, a suspected aneurysm is determined.

In a first specific example of this variation, the segmentation processincludes a dynamic location segmentation process which uses a set ofneural networks (e.g., feed-forward deep CNNs with a U-Net architecture)to process the set of images in a set of multiple stages (e.g., 4stages, 3 stages, 5 stages, etc.), wherein each subsequent stage refinesthe prediction of an aneurysm in the set of images, ultimately resultingin a set of scores associated with the predicted aneurysm(s). Thesescores can then be any or all of aggregated and compared with athreshold.

In a second specific example of this variation, the segmentation processincludes a fixed location segmentation process, which analyzes apredetermined set of regions from the set of images with a neuralnetwork (e.g., feed-forward deep CNN with a U-Net architecture) todetermine a set of scores associated with a predicted likelihood of ananeurysm being predicted in each region (e.g., in the form of asegmentation). These scores can be then any or all of aggregated andcompared with a threshold.

In a third specific example of this variation, the segmentation processincludes both a dynamic location segmentation process (e.g., asdescribed above) and a fixed location segmentation process (e.g., asdescribed below), wherein the scores produced from these processes canbe aggregated and/or compared with one or more thresholds to determine afinal set of predicted aneurysms and any or all of: an associated set ofscores, associated segmentations, and/or any other information.

Additionally or alternatively, S220 can include any other suitableprocesses.

4.4 Method—Triggering an Action Based on the Suspected Condition and/orAssociated Features S230

The method 200 can include triggering an action based on the suspectedcondition and/or associated features S230, which functions to determineand/or provide care for the patient based on the suspected conditionand/or its associated features determined in S220. Additionally oralternatively, S230 can function to enable any or all of: fastertreatment for the patient (e.g., in an event of a detected aneurysm, inan event of a detected critical aneurysm, etc.); better treatment forthe patient (e.g., through the selection of an optimal surgical device,through the selection of the most appropriate treatment, through anautomated path planning process to reach the aneurysm in a surgicalintervention, etc.); better long-term treatment for the patient (e.g.,automated follow-up and/or scheduling of follow-up imaging); and/or anyother improved outcomes.

S230 is preferably performed in response to and based on S220, but canadditionally or alternatively be performed in response to anotherprocess, in response to a trigger, in parallel with other processes ofthe method, prior to any other processes of the method, and/or at anyother times. Further additionally or alternatively, S230 can beperformed multiple times during the method 200, S230 can be performed inabsence of S220, and/or the method 200 can be performed in absence ofS230.

4.5 Method—Determining a Recipient Based on the Suspected Condition S232

S230 can optionally include, in an event that the condition (e.g.,aneurysm) is suspected, determining a recipient based on the suspectedcondition S232, which functions to facilitate the treatment (e.g.,triage, acceptance into a clinical trial, etc.) of the patient.

S232 can additionally, alternatively, and/or equivalently includedetermining a treatment option, preferably in the event that a conditionis detected (e.g., based on a comparison with a threshold, based on abinary presence, etc.) but can additionally or alternatively determine atreatment option when a condition is not detected, when an analysis isinconclusive, or in any suitable scenario. S232 can function to matchthe patient with a specialist, initiate the transfer of a patient to a2^(nd) point of care (e.g., specialist facility), initiate the transferof a specialist to a 1^(st) point of care, initiate treatment of apatient (e.g., surgery, stent placement, etc.) within the 1^(st) pointof care, initiate the matching of a patient to a clinical trial,schedule or tentatively schedule one or more procedures, or perform anyother suitable function. In some variations, the treatment option is a2^(nd) point of care, wherein it is determined (e.g., suggested,assigned, etc.) that the patient should be treated at the 2^(nd) pointof care. Additionally or alternatively, the treatment option can be aprocedure (e.g., surgical procedure, surgical clipping, mechanicalthrombectomy, placement of an aneurysm coil, placement of a stent,retrieval of a thrombus, stereotactic radiosurgery, etc.), treatment,recovery plan (e.g., physical therapy, speech therapy, etc.), or anyother suitable treatment.

The recipient and/or treatment is preferably determined based on aparameter determined from the data packet (e.g., binary presence of acondition, comparison of a parameter with a threshold, etc.), but canadditionally or alternatively be determined based on additional data,such as patient information (e.g., demographic information, patienthistory, patient treatment preferences, etc.), input from one or moreindividuals (e.g., power of attorney, attending physician, emergencyphysician, etc.), a consensus reached by multiple recipients of anotification (e.g., majority of members of a care team, all members of acare team, etc.), or any other suitable information.

S232 is preferably at least partially performed with software operatingat the remote computing system (e.g., remote server) but canadditionally or alternatively be performed at a remote computing systemseparate from a previous remote computing system, a local computingsystem (e.g., local server, virtual machine coupled to healthcarefacility server, computing system connected to a PACS server), or at anyother location.

S232 is preferably performed after a patient condition has beendetermined during the method 200. Additionally or alternatively, S232can be performed after a patient condition has been determined in analternative workflow (e.g., at the 1^(st) point of care, at aradiologist workstation during a standard radiology workflow, in thecase of a false negative, etc.), prior to or absent the determination ofa patient condition (e.g., based on an input from a healthcare worker atthe remote computing system, when patient is admitted to 1^(st) point ofcare, etc.), multiple times throughout the method (e.g., after a firsttreatment option fails, after a first specialist is unresponsive, suchas after a threshold amount of time, such as 30 seconds, 1 minute, 2minutes, etc.), or at any other time during the method.

S232 preferably determines the recipient and/or treatment option with alookup table located in a database accessible at remote computing system(e.g., cloud-computing system). Additionally or alternatively, a lookuptable can be stored at a healthcare facility computing system (e.g.,PACS server), in storage at a user device, or at any other location.

In other variations, the recipient and/or treatment option can bedetermined based on one or more algorithms (e.g., predictive algorithm,trained algorithm, etc.), one or more individuals (e.g., specialist,care team, clinical trial coordinator, etc.), a decision support tool, adecision tree, a set of mappings, a model (e.g., deep learning model),or through any other process or tool.

The lookup table preferably correlates a 2^(nd) point-of-care (e.g.,healthcare facility, hub, physician, specialist, neuro-interventionist,etc.), further preferably a specialist or contact (e.g., administrativeworker, emergency room physician, etc.), with a patient condition (e.g.,presence of an ICH, presence of an LVO, presence of a pathology,severity, etc.), but can additionally or alternatively correlate anytreatment option with the patient condition. The lookup table canfurther additionally or alternatively correlate a treatment option withsupplementary information (e.g., patient history, demographicinformation, heuristic information, etc.).

The recipient (e.g., healthcare provider, neuro-interventionalspecialist, principal investigator, stroke care team member, principalinvestigator, clinical trial enrollment committee, etc.), equivalentlyreferred to herein as a contact, is preferably a healthcare worker, butcan additionally or alternatively be any individual associated with thetreatment of the patient and/or be associated with any healthcarefacility (e.g., prior healthcare facility of patient, current healthcarefacility, recommended healthcare facility) related to the patient. Thecontact is further preferably a specialist (e.g., neuro-interventionalspecialist, neurosurgeon, neurovascular surgeon, general surgeon,cardiac specialist, etc.) but can additionally or alternatively includean administrative worker associated with a specialist, multiple pointsof contact (e.g., ranked order, group, etc.), or any other suitableindividual or group of individuals. The contact is preferably associatedwith a hub facility, wherein the hub facility is determined as an optionfor second point of care, but can additionally or alternatively beassociated with a spoke facility (e.g., current facility, futurefacility option, etc.), an individual with a relation to the patient(e.g., family member, employer, friend, acquaintance, emergency contact,etc.), or any other suitable individual or entity (e.g., employer,insurance company, etc.). Additionally or alternatively, the contact canbe an individual associated with a clinical trial (e.g., principalinvestigator at a 1^(st) point of care, principal investigator at a2^(nd) point of care, approval/enrollment committee to approve a patientfor a clinical trial, etc.), and/or any other suitable individual.

The lookup table is preferably determined based on multiple types ofinformation, such as, but not limited to: location information (e.g.,location of a 1^(st) point of care, location of a 2^(nd) point of care,distance between points of care, etc.), temporal information (e.g., timeof transit between points of care, time passed since patient presentedat 1^(st) point of care, etc.), features of condition (e.g., size ofocclusion, severity of condition, etc.), patient demographics (e.g.,age, general health, history, etc.), specialist information (e.g.,schedule, on-call times, historic response time, skill level, years ofexperience, specialty procedures, historic success or procedures, etc.),healthcare facility information (e.g., current number of patients,available beds, available machines, etc.), but can additionally oralternatively be determined based on a single type of information or inany other suitable way. Information can be actual, estimated, predicted,or otherwise determined or collected.

S232 can include, for instance, any or all of: matching the patient witha specialist, initiating the transfer of a patient to a 2^(nd) point ofcare (e.g., specialist facility), initiate the transfer of a specialistto a 1^(st) point of care, initiate treatment of a patient (e.g.,surgery, stent placement, mechanical thrombectomy, etc.) within the1^(st) point of care, initiating the matching of a patient to a clinicaltrial, or performing any other suitable function. In some variations,the treatment option is a 2^(nd) point of care, wherein it is determined(e.g., suggested, assigned, etc.) that the patient should be treated atthe 2^(nd) point of care. Additionally or alternatively, the treatmentoption can be a procedure (e.g., surgical procedure, surgical clipping,mechanical thrombectomy, placement of an aneurysm coil, placement of astent, retrieval of a thrombus, stereotactic radiosurgery, etc.),treatment (e.g., tissue plasminogen activator (TPA), pain killer, bloodthinner, etc.), recovery plan (e.g., physical therapy, speech therapy,etc.), or any other suitable treatment.

4.6 Method—Preparing a Data Packet for Transfer S234

S230 can optionally additionally or alternatively include preparing adata packet for transfer S234, which functions to produce a compresseddata packet, partially or fully anonymize a data packet (e.g., to complywith patient privacy guidelines, to comply with Health InsurancePortability and Accountability Act (HIPAA) regulations, to comply withGeneral Data Protection Regulation (GDRP) protocols, etc.), minimize thetime to transfer a data packet, annotate one or more images, or performany other suitable function. Additionally or alternatively, any or allof a data packet previously described can be transferred.

The data packet is preferably transferred (e.g., once when data packetis generated, after a predetermined delay, etc.) to a contact, furtherpreferably a specialist (e.g., associated with a 2^(nd) point of care,located at the 1^(st) point of care, etc.), but can additionally oralternatively be sent to another healthcare facility worker (e.g., at1^(st) point of care, radiologist, etc.), an individual (e.g., relative,patient, etc.), a healthcare facility computing system (e.g.,workstation), a server or database (e.g., PACS server), or to any othersuitable location.

S234 preferably includes compressing a set of images (e.g., series), butcan additionally or alternatively leave the set of images uncompressed,compress a partial set of images (e.g., a subset depicting thecondition), or compress any other part of a data packet. Compressing thedata packet functions to enable the data packet to be sent to, receivedat, and viewed on a user device, such as a mobile device. Compressingthe data packet can include any or all of: removing a particular imageregion (e.g., region corresponding to air, region corresponding to hardmatter, region without contrast dye, irrelevant anatomical region,etc.), thresholding of voxel values (e.g., all values below apredetermined threshold are set to a fixed value, all values above apredetermined threshold are set to a fixed value, all values below −500HU are set to −500, all voxel values corresponding to a particularregion are set to a fixed value, all voxels corresponding to air are setto a predetermined fixed value, etc.), reducing a size of each image(e.g., scale image size by factor of 0.9, scale image size by factor of0.7, scale image size by factor of 0.5, scale image size by a factorbetween 0.1 and 0.9, reduce image size by a factor of 4, etc.), orthrough any other compression method.

In one variation, the reduction in size of a set of images can bedetermined based on one or more memory constraints of the receivingdevice (e.g., user device, mobile device, etc.).

In some variations, such as those involving a patient presenting with abrain condition (e.g., aneurysm, ICH, LVO), the images taken at animaging modality (e.g., CT scanner) are compressed by determining anapproximate or exact region in each image corresponding to air (e.g.,based on HU value, based on location, based on volume, etc.) and settingthe air region (e.g., voxels corresponding to the air region, pixelscorresponding to the air region, etc.) to have a fixed value.Additionally or alternatively, any non-critical region (e.g., bone,unaffected region, etc.) or other region can be altered (e.g., set to afixed value, removed, etc.) during the compression. In a specificexample, for instance, a set of voxels corresponding to air are set toall have a common fixed value (e.g., an upper limit value, a lower limitvalue, a value between 0 and 1, a predetermined value, etc.).

In some variations, S234 includes identifying an optimal visualizationto be transmitted (e.g., from a remote computing system) and received(e.g., at a user device), which functions to prepare an optimal outputfor a 2^(nd) point of care (e.g., specialist), reduce the time requiredto review the data packet, bring attention to the most relevant imagedata, or to effect any other suitable outcome.

In some variations, this involves a reverse registration process. In aspecific example, for instance, this is done through maximum intensityprojection (MIP), where an optimal range of instances is determinedbased on which images contain the largest percentage of the segmentedanatomical region of interest in a MIP image.

Additionally or alternatively, S234 can include removing and/or altering(e.g., encrypting) metadata or any unnecessary, private, confidential,or sensitive information from the data packet. In some variations,patient information (e.g., patient-identifiable information) is removedfrom the data packet in order to comply with regulatory guidelines. Inother variations, all metadata are extracted and removed from the datapacket.

S234 can optionally include annotating one or more images in the datapacket, which can function to draw attention to one or more features ofthe images, help a specialist or other recipient easily and efficientlyassess the images, and/or perform any other suitable functions.

Annotating the images can optionally include adding (e.g., assigning,overlaying, etc.) one or more visual indicators (e.g., labels, text,arrows, highlighted or colored regions, measurements, etc.) to one ormore images. The incorporation of the visual indicators can bedetermined based on any or all of: the suspected condition (e.g., typeof visual indicators designated for the condition based on a lookuptable), one or more thresholds (e.g., size thresholds), features of thesuspected condition/pathology (e.g., location of hemorrhage withinbrain), preferences (e.g., specialist preferences, point of carepreferences, etc.), guidelines (e.g., patient privacy guidelines),scores (e.g., risk score, severity score, etc.), and/or any othersuitable information.

Images can additionally or alternatively be annotated with one or moremetrics, such as one or more parameters (e.g., size as described above);scores (e.g., a clinical score, a severity score, etc.); instructions(e.g., recommended intervention); and/or any other suitable information.

Additionally or alternatively to being annotated on an image, any or allof the annotations can be provided in a separate notification, such as amessage, document, and/or provided in any other suitable way.

The annotations are preferably determined automatically (e.g., at aremote computing system implementing the deep learning models, at aclient application, at a mobile device executing a client application,etc.), but can additionally or alternatively be determined manually,verified manually, or otherwise determined.

S234 can optionally include prescribing a subset of images to be viewedby the recipient and/or an order in which images should be viewed (e.g.,the particular image shown first to the recipient upon opening a clientapplication in response to receiving a notification, the image shown inthe thumbnail of a notification, the only image or subset of imagessent, etc.). This can include, for instance, selecting the image orimages indicating the suspected condition (e.g., all slices containingthe suspected condition, a single slice containing the suspectedcondition, the slice containing the largest cross section of a suspectedcondition, the slice containing an important or critical feature of thesuspected condition, etc.) for viewing by the recipient. In specificexamples, the recipient (e.g., specialist) is sent a notificationwherein when the recipient opens the notification on a device (e.g.,mobile device), the image corresponding to the suspected condition isshown first (and optionally corresponds to a thumbnail image shown tothe recipient in the notification).

The notification(s) and/or image(s) provided to a recipient arepreferably provided within a threshold time period from the time inwhich the patient is imaged (e.g., 15 minutes, between 10 and 15minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6 minutes, 5minutes, 3 minutes, 2 minutes, 1 minute, etc.), but can additionally oralternatively be provided in another suitable time frame (e.g., greaterthan 15 minutes), prior to a next action in the standard of care (e.g.,prior to a decision is made by a radiologist in a parallel workflow),and/or at any other time(s).

In some variations, S234 includes storing a dataset (e.g., at a remoteserver, at a local server, at a PACS server, etc.). In one example,metadata are extracted from the image data and stored separately fromimage data in a relational database. In another example, any or all ofthe data packet are stored (e.g., temporarily, permanently, etc.) to beused in one or more future analytics processes, which can function toimprove the method, better match patients with suitable treatmentoptions, or for any other suitable purpose.

S234 can optionally include applying a low bandwidth implementationprocess, which can function to reduce the time until a specialistreceives a first piece of data or data packet (e.g., an incompleteseries, incomplete study, single instance, single image, optimal image,image showing occlusion, etc.), reduce the processing required to informa specialist of a potential patient condition, reduce the amount of datarequired to be reviewed by a specialist, reduce the amount of data beingtransmitted from a remote computing system to a mobile device, orperform any other suitable function. The low bandwidth implementationprocess can include any or all of: organizing (e.g., chunking) data(e.g., chunking a series of images based on anatomical region),reordering data (e.g., reordering slices in a CT series), transmitting aportion (e.g., single image, single slice, etc.) of a data packet (e.g.,series, study, set of images, etc.) to a device (e.g., user device,mobile device, healthcare facility workstation, computer, etc.), sendingthe rest of the data packet (e.g., only in response to a request, aftera predetermined time has passed, once the data packet has been fullyprocessed, etc.), or any other process. In a specific example, forinstance, the image data (e.g., slices) received at a remote computingsystem from a scanner are chunked, reordered, and a subset of slices(e.g., a single slice) is sent to the device associated with aspecialist (e.g., prior to sending a remaining set of slices, in absenceof sending a remaining set of slices, etc.).

In some variations, S234 includes implementing a buffering protocol,which enables a recipient (e.g., specialist) to start viewing images(e.g., on a mobile device) prior to all of the images being loaded atthe device (e.g., user device, mobile device, etc.) at which therecipient is viewing images. Additionally or alternatively, thebuffering protocol can include transmitting images to the recipient inbatches, annotating images in batches, or otherwise implementing abuffering protocol.

Additionally or alternatively, S234 can include any other suitable stepsperformed in any suitable order.

The method can additionally or alternatively include any other suitablesub-steps for preparing the data packet.

In a first set of variations, S234 includes determining a subset of oneor more images conveying information related to a suspected patientcondition (e.g., showing the presence of the condition, showing thelargest region of the condition, showing a particular feature of thecondition, etc.); optionally annotating the images (e.g., to point outthe condition); and preparing a notification to be sent to thespecialist, wherein the notification instructs the specialist to view atleast the subset of images and optionally includes a thumbnail depictingone of the set of images which the specialist can view prior to viewingthe images.

4.7 Method—Transmitting Information to a Device Associated with theRecipient S236

S230 can optionally additionally or alternatively include transmittinginformation to a device S236, which functions to convey information torecipient of the device and to receive one or more inputs from therecipient.

The device is preferably associated with (e.g., owned by, belonging to,accessible by, etc.) a specialist or other individual associated withthe 2^(nd) point of care, but can additionally or alternatively beassociated with an individual or computing system at the 1^(st) point ofcare, the patient, or any other suitable individual or system.

In one variation, the device is a personal mobile phone of a specialist.In another variation, the device is a workstation at a healthcarefacility (e.g., first point of care, second point of care, etc.).

In some variations, information is sent to multiple members (e.g., allmembers) of a care team or clinical trial team, such as the care teamwho is treating, may treat the patient, or and/or will treat thepatient. This can enable the care team members to do any or all of: makea decision together (e.g., transfer decision, treatment decision, etc.);communicate together (e.g., through the client application); and/orperform any other function.

The information preferably includes a data packet, further preferablythe data packet prepared in S234. Additionally or alternatively, theinformation can include a subset of a data packet, the original datapacket, any other image data set, or any other suitable data. Theinformation further preferably includes a notification, wherein thenotification prompts the individual to review the data packet at thedevice (e.g., a message reciting “urgent: please review!”). Thenotification can optionally include a thumbnail with a selected image(e.g., image indicating patient condition), which a recipient can viewquickly, such as prior to the recipient unlocking device to which thenotification is sent. Additionally or alternatively, the notificationcan prompt the individual to review data (e.g., original data packet,uncompressed images, etc.) at a separate device, such as a workstationin a healthcare facility, a PACS server, or any other location. Furtheradditionally or alternatively, the notification can include any suitableinformation, such as, but not limited to: instructions (e.g., fortreating patient, directions for reaching a healthcare facility),contact information (e.g., for emergency physician at first point ofcare, administrative assistant, etc.), patient information (e.g.,patient history), or any other suitable information.

The notification preferably includes an SMS text message but canadditionally or alternatively include a message through a clientapplication (e.g., as described above, image viewing application,medical imaging application, etc.), an email message (e.g.,de-identified email), audio notification or message (e.g., recordingsent to mobile phone), push notification, phone call, a notificationthrough a medical platform (e.g., PACS, EHR, EMR, healthcare facilitydatabase, etc.), pager, or any other suitable notification.

One or more features of a notification can optionally convey a severityof the patient condition and/or an urgency of receiving a response froma recipient, which can function to adequately alert the recipient andproperly prioritize care of the patient (e.g., relative to otherpatients). In specific examples, for instance, an audio cue associatedwith a notification indicates an urgency of treating a patient, so thata recipient of the message knows to immediately review the images andtriage the patient.

The information is preferably sent to the device through a clientapplication executing on the user device but can additionally oralternatively be sent through a messaging platform, web browser, orother platform. In some variations, the information is sent to alldevices (e.g., mobile phone, smart watch, laptop, tablet, workstation,etc.) associated with the recipient (e.g., specialist), such as alldevices executing the client application associated with the recipient,which functions to increase the immediacy in which the recipient isnotified.

S234 can optionally include preparing a notification to be sent to adevice (e.g., user device, mobile device, etc.) associated with arecipient (e.g., a specialist), wherein the notification includes athumbnail indicating a selected image (e.g., compressed image showing asuspected condition), along with a message instructing the recipient toreview the images in a client application, and optionally the originalimages at a workstation afterward. In a first set of specific examples,upon detection that a read receipt has not been received (e.g., at theremote computing system) in a predetermined amount of time (e.g., 30seconds, 1 minute, 2 minutes, between 0 seconds and 2 minutes, 3minutes, between 2 minutes and 3 minutes, 5 minutes, greater than 5minutes, less than 10 minutes, etc.), a second notification istransmitted to a second recipient (e.g., a second specialist). In asecond set of specific examples, sending the notification furthertriggers and/or enables communication to be established among multiplemembers of a care team (e.g., a stroke team), such as through amessaging component of the client application, wherein the images can beviewed and discussed among the care team members. In a third set ofspecific examples, a notification is sent to specialist on a mobiledevice of the specialist, compressed images are previewed on thespecialist mobile device, and the specialist is notified as beingresponsible for viewing non-compressed images on a diagnostic viewer andengaging in appropriate patient evaluation and relevant discussion witha treating physician before making care-related decisions or requests.

Transmitting information to a device associated with the 2^(nd) point ofcare (e.g., specialist, contact, etc.) S236 (e.g., as shown in FIG. 6)functions to initiate a pull from a 1^(st) point of care to a 2^(nd)point of care, which can decrease time to care, improve quality of care(e.g., better match between patient condition and specialist), or haveany other suitable outcome. Preferably, the 2^(nd) point of care is ahub facility (e.g., specialist facility, interventional center,comprehensive stroke center, etc.). In some variations, the 1^(st) pointof care (e.g., healthcare facility at which patient initially presents)also functions as the 2^(nd) point of care, such as when a suitablespecialist is associated with the 1^(st) point of care, the 1^(st) pointof care is a hub (e.g., specialist facility, interventional center,comprehensive stroke center, etc.), it is not advised to transfer thepatient (e.g., condition has high severity based on a calculatedseverity score), or for any other reason.

S236 is preferably performed after (e.g., in response to) a 2^(nd) pointof care is determined, but can additionally or alternatively beperformed after a data packet (e.g., compressed data packet, encrypteddata packet, etc.) has been determined, multiple times throughout themethod (e.g., to multiple recipients, with multiple data packets, withupdated information, after a predetermined amount of time has passedsince a notification has been sent to a first choice specialist, etc.),or at any other time during the method 200.

In a first set of variations, the recipient includes a specialist,preferably a neurovascular and/or a neurosurgical specialist, and thetransmitted information includes a notification sent to a mobile userdevice of the specialist, the notification indicating that a suspectedaneurysm has been identified and recommending review of the patient'simages (e.g., at a mobile application executing on the mobile userdevice, at a workstation of the specialist, at the mobile applicationand then the workstation, etc.). In specific examples, a compressedversion of the images are viewable at the mobile application, whereinthe specialist is recommended to view non-compressed images on adiagnostic viewer (equivalently referred to herein as a workstation) toevaluate the patient (e.g., discuss with a treating physician, makecare-related decisions and/or requests, etc.). In additional oralternative specific examples, non-compressed images can be viewed atthe mobile device, compressed images can be viewed at the diagnosticviewer, and/or images of any type can be viewed at any suitable devicesor combination of devices.

In a second set of variations, S234 includes preparing a notification tobe sent to a clinical trial research coordinator, such as a principalinvestigator, wherein the notification indicates that the patient is apotential candidate for a clinical trial (e.g., based on the detectionof a suspected condition, based on a set of clinical trial inclusioncriteria, etc.). In specific examples, a notification can be sent (e.g.,automatically, triggered by the principal investigator, etc.) to amembers of a clinical trial committee (e.g., physician committee),wherein approval is granted by the committee members (e.g., a majority,all, at least one, a predetermined number or percentage, etc.), such asthrough the client application.

4.8 Method—Receiving an Input from the Recipient and Triggering anAction Based on the Input S238

S230 can optionally additionally or alternatively include receiving aninput from the recipient and triggering an action based on the inputS238, which functions to determine a next step for the patient, and caninclude any or all of: a confirmation of the suspected condition; arejection of the suspected condition (e.g., false positive); anacceptance by a specialist and/or care team (e.g., stroke team) to treatthe patient (e.g., at a 1^(st) point of care, at a 2^(nd) point of care,etc.); a rejection of a specialist and/or care team to treat thepatient; a read receipt and/or an indication of a lack or a read receiptwithin a predetermined time threshold; an approval to enroll the patientin a clinical trial; additional clinical information entered by aphysician and/or other user; and/or any other suitable input.

In some variations, a notification is sent in S236 which prompts theindividual to provide an input, wherein the input can indicate that theindividual will view, has viewed, or is in the process of viewing theinformation (e.g., image data), sees the presence of a condition (e.g.,true positive, serious condition, time-sensitive condition, etc.), doesnot see the presence of a condition (e.g., false positive, seriouscondition, time-sensitive condition, etc.), has accepted treatment ofthe patient (e.g., swipes right, swipes up, clicks a check mark, etc.),has denied treatment of the patient (e.g., swipes left, swipes down,clicks an ‘x’, etc.), wants to communicate with another individual(e.g., healthcare worker at 1^(st) point of care), such as through amessaging platform (e.g., native to the device, enabled by the clientapplication, etc.), or any other input. In some variations, one or moreadditional notifications are provided to the individual (e.g., based onthe contents of the input), which can be determined by a lookup table,operator, individual, decision engine, or other tool. In one example,for instance, if the individual indicates that the condition is a truepositive, information related to the transfer of the patient (e.g.,estimated time of arrival, directions to the location of the patient,etc.) can be provided (e.g., in a transfer request, wherein patienttransfer to a specified location, such as the 2^(nd) point of care, canbe initiated upon transfer request receipt). In some variants, the data(e.g., images) are displayed on the user device (e.g., mobile device,workstation) in response to user interaction with the notification(e.g., in response to input receipt). However, the input can trigger anysuitable action or be otherwise used.

Additionally or alternatively, an input can automatically be receivedfrom the client application, such as a read receipt when the individualhas opened the data packet, viewed the notification, or interacted withthe client application in any other suitable way. In one example, if aread receipt is not received (e.g., at the remote computing system) fromthe device within a predetermined amount of time (e.g., 10 seconds), asecond notification and/or data packet (e.g., compressed set of images)are sent to a second individual (e.g., second choice specialist based ona lookup table).

In some variations, various outputs can be sent from the clientapplication (e.g., at the user device) to one or more recipients (e.g.,to a second user device, client application on a work station, on acomputing system, etc.), such as recipients associated with a firstpoint of care (e.g., radiologists, emergency physicians, etc.). Theoutputs can be determined based on the inputs received at the clientapplication associated with the individual (e.g., acceptance of case,verification of true positive, etc.), based on a lookup table, orotherwise determined. The outputs preferably do not alter the standardradiology workflow (e.g., are not shared with radiologists; radiologistsare not notified), which functions to ensure that the method 200 is atrue parallel process, and that the standard radiology workflow resultsin an independent assessment of the patient, but can additionally oralternatively cut short a workflow, bring a specialist in on the patientcase earlier than normal, or affect any other process in a healthcarefacility.

The outputs can include any or all of: the suspected condition,parameters (e.g., volume of an ICH) and/or scores (e.g., severity score,urgency score, etc.) associated with the suspected condition; theselection of one or more recipients of a notification (e.g., establishedand/or proposed care team of the patient); a proposed and/or confirmedintervention for the patient (e.g., type of procedure); an updatedstatus (e.g., location, health status, intervention status, etc.) of oneor more patients (e.g., a centralized list of all patients beingreviewed by and/or treated by a specialist; a consent of the patient(e.g., for a clinical trial); an estimated parameter of the patient(e.g., estimated time of arrival at a second point of care); and/or anyother suitable outputs.

The method can additionally or alternatively include initiatingtreatment (e.g., transfer) of the patient, wherein the treatment caninclude any or all of the treatment options described above, such as ayor all of: a point of care (e.g., remain at 1^(st) point of care, betransferred to a 2^(nd) point of care, etc.) at which the patient willbe treated; a procedure to treat the suspected condition; a specialistand/or care team to be assigned to the patient; a clinical trial inwhich to enroll the patient; and/or any other suitable treatments.

In variations involving recommending the patient for a clinical trial,initiating treatment of the patient can include receiving arecommendation that the patient be considered for a clinical and/orresearch trial, based on one or more of: a suspected clinical conditionof the patient (e.g., ICH), patient information (e.g., demographicinformation), a patient's willingness or potential willingness toparticipate, and/or any other suitable information. Initiating therecommendation can include transmitting any or all of the notificationsdescribed above (e.g., text message, call, email, etc.) to a specialistinvolved in the clinical and/or research trial, a specialist who hasactively turned on notifications for clinical trial recruitment, aresearcher, a research principal investigator, an administrativeassistant, the patient himself, or any other suitable entity orindividual.

In specific examples (e.g., as shown in FIG. 7, as shown in FIG. 9, asperformed in accordance with a system shown in FIG. 10, etc.),additional or alternative to those described above, the method functionsto evaluate if a patient presenting with a potential pathology (e.g.,aneurysm, stroke, ICH, LVO, etc.) qualifies for a clinical trial and ifso, to alert (e.g., automatically, in a time period shorter than adetermination made by a radiologist in a standard radiology workflow,etc.) a research coordinator (e.g., principal investigator) associatedwith the clinical trial (e.g., as shown in FIG. 8).

The method can additionally or alternatively include establishingcommunication between users (e.g., texting, call, HIPAA-complianttexting, HIPAA-compliant calling, video call, etc.), such as between anyor all of: multiple healthcare workers (e.g., physicians, surgeons,surgical technicians responsible for prepping for a surgical procedure,etc.), multiple research coordinators (e.g., from the same clinicaltrial, from different clinical trials, etc.), a healthcare worker and aresearch coordinator (e.g., for the research coordinator to askquestions from the surgeon, as shown in FIG. 11, etc.), a researchcoordinator and a patient (e.g., to submit a consent form to thepatient, to receive a consent form from the patient, etc.), a healthcareworker and a patient, and/or between any other suitable users andindividuals.

Additionally or alternatively, the action can include any or all of:automatically selecting and/or recommending an optimal surgicalprocedure for the patient (e.g., based on the location of a suspectedaneurysm, based on a size of a suspected aneurysm, etc.); automaticallyselecting and/or recommending an optimal medical device (e.g., catheterlength, aneurysm coil, etc.) for a surgical procedure; automaticallyassembling a surgical team for surgery of the patient (e.g., byautomatically establishing a communication thread between members of thesurgical care team); and/or any other processes.

4.9 Method—Aggregating Data S238

The method 200 can optionally include any number of sub-steps involvingthe aggregation of data involved in and/or generated during the method200, which can function to improve future iterations of the method 200(e.g., better match patients with a specialist, decrease time to treat apatient, increase sensitivity, increase specificity, etc.). Theaggregated data is preferably used in one or more analytics steps (e.g.,to refine a treatment option, make a recommendation for a drug orprocedure, etc.), but can additionally or alternatively be used for anyother suitable purpose. In some variations, for instance, aggregateddata is used to train and/or re-train one or more trained models (e.g.,machine learning models, deep learning models, neural networks, etc.)used in the method 200.

In a first set of variations, the outcomes of the patients examinedduring the method 200 are recorded and correlated with theircorresponding data packets, which can be used to assess the success ofthe particular treatment options chosen and better inform treatmentoptions in future cases.

In specific examples, these outcomes along with the set of inputs usedto determine these outcomes are analyzed and used to retrain any or allof the trained models used to reach a set of outcomes.

4.10 Variations

In a first set of variations, detecting an aneurysm in the method 200includes filtering a set of images based on associated metadata;preprocessing the set of images; registering the set of images, andsegmenting the set of images.

In specific examples, the method 200 includes: receiving a set of imagescorresponding to a CTA head scan of a patient; verifying that the set ofimages is applicable to the method 22 by inspecting DICOM metadata tagsand determining that the set of images satisfies all of a predeterminedset of metadata inclusion criteria; checking for a set of exclusioncriteria (e.g., metallic artifact), wherein upon determining that apredetermined number (e.g., 1, 2, 10, between 10 and 100, greater than100, etc.) of voxels of the set of images has an HU value above apredetermined threshold (e.g., 3000 HU), the set of images is eliminatedfrom further processing; cropping the set of images to keep only arelevant region or regions (e.g., brain region, head region, etc.) ofthe patient; applying a clipping transformation to the set of images toremove irrelevant HU values below a predetermined threshold (e.g.,between −500 and −520, less then −500, etc.) and above a predeterminedthreshold (e.g., greater than 1000, between 1000 and 1100, etc.);normalizing the HU values of the set of images based on the mean HUvalue and the standard deviation (e.g., subtracting the mean HU valueand dividing by the standard deviation); optionally resampling the setof images to a predetermined resolution (e.g., 1 mm) in all dimensions;optionally cropping the set of images to include a Circle of Willisregion through a z-axis cropping (e.g., from the top of the scan) and anx-y plane cropping (e.g., from the center of the scan); comparing theset of images (e.g., the Circle of Willis region) with an atlas in aregistration process including a deep learning model (e.g., deep CNN),wherein the registration produces a transformation to apply to theimages (e.g., the original images); optionally cropping and/orresampling the images; segmenting the images with a deep learning model(e.g., a U-net) to determine the presence of a suspected aneurysm andits location; calculating a probability score associated with each voxelcorresponding to the suspected aneurysm; summing the probability scoresand comparing with the threshold; in an event that the scores are abovethe threshold, determining an action, which can optionally include anyor all of: determining a recipient (e.g., specialist, researchcoordinator, etc.); transmitting information to a device associated withthe recipient; receiving an input associated with and/or from therecipient (e.g., read receipt, input, etc.) and optionally triggering anaction based on the input (e.g., initiating transfer, identifying aclinical trial, etc.). Additionally or alternatively, any or all of theaction (e.g., recommending a procedure, recommending a surgical device,etc.) can be performed in absence of determining a recipient.

In a second set of variations (e.g., as shown in FIG. 12), additional oralternative to the first, the method 200 includes receiving a set ofimages (e.g., CTA images) from an imaging modality; optionally filteringthe set of images based on one or more pieces of metadata associatedwith the images; optionally filtering the set of images based on one ormore image-based filtering processes (e.g., based on HU values)optionally pre-processing the set of images (e.g., cropping the set ofimages, resampling the set of images, etc.); performing a registrationprocess for the set of images (e.g., the preprocessed set of images)with a set of one or more neural networks to determine a transformation(e.g., affine transformation) for the set of images; applying thetransformation to the set of images; performing a set of one or moresegmentation processes to detect a suspected set of aneurysms and anyfeatures (e.g., size, location, etc.) associated with the set ofaneurysms; determining a set of scores based on and/or with thesegmentation processes; optionally aggregating multiple sets of scores(e.g., from multiple segmentation processes, from multiple suspectedaneurysms, from multiple regions of the set of images processedindependently, etc.); optionally comparing the set(s) of scores with oneor more thresholds to determine and/or confirm a suspected aneurysmand/or aneurysm features; and triggering an action based on thesuspected aneurysm and/or features.

In a first set of specific examples, the set of segmentation processesincludes a fixed location segmentation process (e.g., as describedabove).

In a second set of specific examples, the set of segmentation processesincludes a dynamic location segmentation process (e.g., as describedabove).

In a third set of specific examples, the set of segmentation processesincludes a dynamic location segmentation process (e.g., as describedabove) and a fixed location segmentation process (e.g., as describedabove), wherein output scores from these segmentation processes can beaggregated and used in determining the set of suspected aneurysms.Additionally or alternatively, the scores can be non-aggregated and usedindependently to assess for suspected aneurysms.

Additionally or alternatively, the method can include any other suitableprocesses.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes, wherein the method processes can beperformed in any suitable order, sequentially or concurrently.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for computer-aided detection and decision-makingassociated with a suspected pathology, the method comprising: receivinga set of images associated with a patient; processing the set of imageswith a set of trained models to automatically identify the suspectedpathology; in response to automatically identifying the suspectedpathology, triggering an action at a mobile user device associated witha specialist; and monitoring for an input from the specialist at themobile user device, wherein in an event that the input is not receivedwithin a predetermined time threshold, triggering a second action at asecond mobile user device associated with a second specialist.
 2. Themethod of claim 1, wherein the suspected pathology is a suspectedaneurysm, and wherein processing the set of images with the set oftrained models comprises a set of multiple segmentation processes. 3.The method of claim 2, wherein the set of multiple segmentationprocesses comprises: a first segmentation process performed with a firstsubset of the set of trained models; and a second segmentation processperformed with a second subset of the set of trained models.
 4. Themethod of claim 3, wherein: the first segmentation process produces afirst set of probability scores; and the second segmentation processproduces a second set of probability scores.
 5. The method of claim 4,further comprising: aggregating the first and second sets of probabilityscores to determine an aggregated set of probability scores; comparingthe aggregated set of probability scores with a predetermined set ofthresholds; and automatically identifying the suspected aneurysm basedon the comparison.
 6. The method of claim 1, wherein triggering theaction at the mobile user device comprises automatically determining andtransmitting a notification to an application executing on the mobileuser device.
 7. The method of claim 6, wherein triggering the secondaction comprises transmitting the notification to the second mobile userdevice.
 8. The method of claim 1, wherein each of the first and secondactions further comprises transmitting a second set of images to thefirst and second mobile user devices, the second set of imagesdetermined based on the first set of images.
 9. The method of claim 8,wherein the second set of images is a compressed version of the firstset of images.
 10. The method of claim 8, wherein the second set ofimages is a subset of the first set of images.
 11. The method of claim8, wherein monitoring for the input from the specialist furthercomprises, in response to receiving the input, the method furthercomprises initiating a transfer of the patient.
 12. The method of claim11, wherein the set of images is taken at an imaging modality located ata first point of care, wherein the specialist is associated with asecond point of care, and wherein the transfer is from the first pointof care to the second point of care.
 13. The method of claim 12, whereinthe method further comprises, in response to receiving the input,assigning treatment of the patient to the specialist.
 14. A system forcomputer-aided detection and decision-making associated with a suspectedpathology, the system comprising: a computing subsystem, wherein thecomputing subsystem: receives a set of images associated with a patient;processes the set of images with a set of trained models toautomatically identify the suspected pathology; a first applicationexecuting on a first mobile user device associated with a firstspecialist, wherein the first application: in response to identificationof the suspected pathology, triggers an action at the first mobile userdevice; monitors for an input from the first specialist; and a secondapplication associated with a second specialist, wherein the secondapplication: in an event that the input is not received within apredetermined time threshold, triggers a second action.
 15. The systemof claim 14, wherein the set of trained models comprises: a first set ofneural networks configured to perform a first segmentation process; anda second set of neural networks configured to perform a secondsegmentation process.
 16. The system of claim 15, wherein the firstsegmentation process is a single stage segmentation process and whereinthe second segmentation process is a multi-stage segmentation process.17. The system of claim 16, further comprising processing the set ofimages with a third set of neural networks to determine a predeterminedset of anatomical regions, wherein the first segmentation process isperformed based on the predetermined set of anatomical regions.
 18. Thesystem of claim 14, wherein the second application is executing on asecond mobile user device associated with the second specialist, whereinthe second action is triggered at the second mobile user device.
 19. Thesystem of claim 14, wherein the first application, in response toreceiving the input, automatically initiates a transfer of the patientfrom a first point of care to a second point of care located remote fromthe first point of care.
 20. The system of claim 14, wherein each of thefirst and second actions comprises the transmission of a notificationand a second set of images, the second set of images determined based onthe first set of images, to at least one of the first mobile user deviceand the second mobile user device.